For Semiconductor Companies

Live mission command for silicon programs. Governed. Auditable.

Tymeline gives engineering leaders a single live picture of their program — from RTL freeze to GDSII signoff to post-silicon. Your engineers stay your engineers. Each team gets a hybrid teammate — a persistent AI Employee that handles the coordination, retrieval, and synthesis work, so your humans spend their day on the engineering only humans can do. Scenario simulation before you commit. RCA-grade audit on every decision. Your design IP and existing engineering rituals — untouched.

Book a Semiconductor Briefing → See the Hybrid Team Math
RTL → GDSII → Post-Si SOC 2 · ISO 27001 · GDPR SaaS · VPC · Air-gap Audit-grade decision trail
HYBRID TEAM ROSTER · DESIGN-CENTER-3132 ACTIVE
RTL · DESIGN
17 + Aria
BLOCKS OWNED
7 IN ACTIVE DEV
VERIFICATION
26 + Vela
COVERAGE
94.2% CLOSURE
DFT
7 + Dax
ATPG PATTERNS
2.4M GENERATED
PHYSICAL DESIGN
18 + Phin
TIMING SLACK
+12ps WNS
ANALOG · MIXED-SIG
12 + Ana
PHY BLOCKS
3 IN BRINGUP
POST-SILICON
14 + Posi
BUGS TRIAGED
147 / WEEK
The Re-Spin Math

One missed handoff. One re-spin. A quarter of revenue, gone.

Industry analyses consistently identify coordination and specification drift, not novel physics, as the dominant cause of semiconductor re-spins. The data exists across Jira, Cadence, Synopsys, Perforce, and Confluence — but no human or copilot is wiring it together in real time.

$540M
Average cost · 5nm SoC
Total design cost for a leading-node SoC at 5nm, requiring ~864 engineer-days. Cost grows 2–3× per node generation.
SOURCE: McKinsey via AWS · 2024
$5–30M
Mask set cost · single re-spin
Mask costs alone for a leading-node re-spin, before factoring foundry slot re-booking, wafer fab, bring-up, and lost market window.
SOURCE: Industry consensus · multiple foundry data
~70%
Re-spins from coordination gaps
Verification handoff drift, specification mismatch, IP integration error — root cause is almost always organizational visibility, not engineering capability.
SOURCE: Industry analyses · DAC / DVCon historical
The Tymeline Promise · In One Sentence
Tymeline does not replace your engineers.
It removes the coordination work that keeps engineers from doing engineering.
0
Headcount cuts assumed
~30%
Engineer time spent on coordination today
100%
Of consequential decisions stay with humans
1 + 1
Hybrid team unit · human + AI Employee
The AI Employee Architecture

Not a copilot. Not a chatbot. A persistent team member with cognition that survives the program.

Most enterprise AI tools forget. They answer one question, then start over. A semiconductor program runs eighteen to thirty months across thousands of dependencies — not a use case for a stateless chatbot. Tymeline's AI Employees are deployed once, embedded in a team, and accumulate continuous knowledge of that team's blocks, decisions, dependencies, and engineers across the full program lifecycle.

FIVE-LAYER PERSISTENCE MODELDECISION-GRADE COGNITION
01
M
Memory
Episodic record of every event the AI Employee has observed across the entire program lifetime — handoffs, regressions, ECOs, design reviews, bug filings, engineer assignments.
Layered: working · episodic · semantic · procedural
02
C
Concept
Domain ontology of silicon engineering. Knows what UVM coverage closure means, what a DFT signoff requires, why Liberty timing files matter. Not generic project vocabulary.
Built on: open-source LLMs · domain ontologies · your specs
03
X
Context
Live state of your program: which blocks are owned by whom, which regressions ran last night, which licenses are saturated, which milestones are slipping, which engineers are over-allocated.
Sourced from: Jira · Perforce · Cadence · Synopsys · Confluence
04
I
Intent
The leadership directive the agent operates under. Tape out by Q3. Hold WNS above zero. Keep verification ahead of RTL freeze. No agent acts outside its declared intent scope.
Set by: VP Eng · program lead · explicit policy
05
P
Provenance
Cryptographic trail of which data was retrieved, which policy was consulted, what reasoning chain was followed, whether human approval was invoked. Every action defensible in audit.
Anchored on: blockchain · tamper-evident logs · Tymeline ID
How It Works · The Way Engineers Already Work

Autonomous where it makes sense. Human where it matters.

A common worry with AI agents is that they impose a new way of working — a new tool, a new ritual, a new place to check, a new approval flow. Tymeline does the opposite. AI Employees show up to the engineering rituals you already run, in the formats you already use, through the channels your team already lives in. The autonomy is in the preparation and the follow-through. The decisions stay where they belong: with the engineer, the lead, and the program manager.

"Engineers don't have to learn a new way to work. The AI Employee learns yours."
Daily Standup

The standup runs the way it always has.

Fifteen minutes, your team, your channel — Teams, Slack, conference room. The AI Employee doesn't run the meeting. It shows up to it.

What the AI Employee does: drafts the standup digest beforehand — overnight regression deltas, blocked tickets, dependencies that shifted — so the humans spend the meeting deciding, not reporting.
Design Review

The review board is the same people, the same gate criteria.

You still walk through the spec. The architect still asks the hard questions. The methodology team still owns sign-off.

What the AI Employee does: assembles the review packet in your standard format — change summary, coverage delta, risk flags, prior-decision references — so reviewers walk in prepared.
Bug Triage

Bug triage stays in your tracker, with your owners.

Jira stays Jira. Your severity ladder stays yours. The block owner is still the block owner.

What the AI Employee does: pre-triages incoming defects — classifies by likely root cause, finds prior similar bugs, suggests an owner — and lets the human triage call confirm or override.
ECO & Change Board

The change board still approves every ECO.

Your existing change control process is untouched. The CCB meeting still happens. The signed-off approvers are still the signed-off approvers.

What the AI Employee does: models downstream impact across power, timing, area, and DRC the moment an ECO is proposed — so the CCB sees the full ripple effect, not a guess.
Tape-Out Gate Review

The gate review is the gate review. Named approvers. Formal sign-off.

No AI Employee approves a tape-out gate. Ever. That's policy, enforced at runtime — not a marketing line.

What the AI Employee does: compiles the gate readiness packet across every block, surfaces residual risks with documented owners, and pre-fills the sign-off template you already use.
Retrospective & RCA

Retros and RCAs run with the team in the room.

The conversations that matter — about what went wrong, what to learn, what to change — happen between engineers, not between humans and a chatbot.

What the AI Employee does: replays the full decision trail in 2 minutes — first signal, who knew, when, what was deferred — so the retro discussion is about the lesson, not the forensics.

The principle: Tymeline runs autonomously where the work is preparation, retrieval, and synthesis — the parts engineers find tedious. Humans stay in charge of every decision that ships. Approvers are named. Approvals are MFA-verified. The rituals you trust are the rituals that govern.

The Eight Scenarios That Decide Tape-Out

What a program leader actually asks Tymeline. And the answer they get back.

Eight recurring decision moments define whether a silicon program ships clean. In each one, Tymeline's multi-agent fabric does the synthesis no human team can do at speed — pulling live signals from every block, simulating second-order impact, and handing the leader a defensible decision packet. Three command altitudes: Plan the next move. Execute in real time. Learn from what happened.

Cluster 01 · Plan
Run the next eight weeks before they happen.
"Can we pull tape-out in by three weeks? Sales just asked."
Path A · Reject
Hold T-19. Tell sales no.
DDR5 controller, USB4 host, and DSP cluster all on critical path. No engineering capacity slack inside the program.
Tape-out probability 0.91 · revenue impact −
Path B · Hybrid pull-in
Pull in 8 days, not 21.
Reallocate 2 senior PD engineers from adjacent program; defer non-critical USB4 verification corners; accept documented residual risk.
Tape-out probability 0.74 · revenue +1 quarter
Path C · Recommended
Pull in 14 days — clean.
Pull-in achievable with 2 PD reallocations and parallel DV regression on cloud burst. No residual risk acceptance required.
Tape-out probability 0.83 · revenue +1 quarter · go
PHIN · VELA · ORIN · SORA · multi-agent simulation
Three branches modeled in 38 seconds
"Third-party SerDes IP just slipped four weeks. What breaks?"
Sora maps every block consuming the SerDes interface and propagates the slip across the dependency graph. Phin re-runs floorplan timing on affected blocks. Pax checks substrate and bump map dependencies. Three blocks absorb the slip cleanly. One — the high-speed coherent fabric — does not.
Decision packet ready in 90 seconds with full impact analysis, suggested mitigations, and a draft escalation note to the IP vendor.
SORA · PHIN · PAX
90-second cascade analysis
Downstream Impact · 4-week SerDes slip
PCIe
+2d
USB4
+3d
Ethernet
+9d
CHI Fabric
+28d
DDR5
+1d
PMU
+0d
"What's the right hybrid team composition for the next program?"
Role
Senior
Mid
Junior
AI Employee
RTL Design
3
8
6
1 · Aria
Verification
4
12
10
1 · Vela
DFT
2
3
2
1 · Dax
Physical Design
5
9
4
1 · Phin
SoC Integration
2
4
2
1 · Sora
Composition derived from prior-program telemetry: junior-heavy verification ramp accelerated by Vela's coverage forecasting; senior PD lean offset by Phin's ECO impact analysis. Total: 76 humans + 5 AI Employees. Projected savings vs. baseline: $4.2M / year.
ORIN · all team agents · cross-program telemetry
Composition modeled against last 3 programs
Cluster 02 · Execute
Decide in the moment — with the full picture.
"Is the SoC actually ready for tape-out — right now?"
Live · queried at 14:22
CPU Core
100%
Ready
L2 Cache
100%
Ready
DDR5 Ctrl
87%
Verif lag
PCIe PHY
98%
Ready
USB4 Host
62%
Blocked
DSP
96%
Ready
PMU
99%
Ready
SerDes
81%
PVT gap
SEC Encl
100%
Ready
NoC
94%
Ready
Composite Readiness · 91.7% · Conditional Go
Two blocks gating tape-out: USB4 host (verification blocked on link-training corners), SerDes (PVT corners 1.0V worst-case incomplete). Phin recommends 5-day defer to clear both. Aria, Vela, Phin, and Posi each contributed signals to this composite. No human assembly of slides required.
"Two programs need the same senior PD lead Monday. Who wins?"
Program A · VP Engineering
Needs senior PD lead M. Singh on congestion debug for top-level integration. Tape-out gate in 12 days.
Slack: 2 days · criticality: high
vs.
Program B · Director, PD
Needs same M. Singh on final timing closure for SerDes block. Pre-signoff review Wednesday.
Slack: 5 days · criticality: medium
Resolution · Routed to Eng Operations
M. Singh assigned to Program A Mon–Tue. Program B's SerDes timing handled by R. Patel (95% qualified per verified skill profile, current load 38%, prior closure on similar block). Both VPs notified with reasoning chain. M. Singh available for Program B Wed AM. No leadership negotiation required.
ORIN · PHIN (×2) · cross-program capacity arbitration
Resolved in 22 seconds
"Tier-1 customer just escalated a bug on engineering samples. What do we know?"
Evidence · what Posi already knows
Symptom signature matches 3 prior bugs on same IP familySource: Posi memory · prior programs 2024–2025
Lab measurement deviates from sim by 12% on VminSource: characterization DB · sample serial 0x4A11
RTL change in clock-gating block 14 days before tape-outSource: Aria · commit ref a91b2c
Coverage gap documented on Vmin corner — deferredSource: Vela archive · regression run 9281
Routing · who owns the next 24 hours
Block owner (clock-gating) → debug ownershipAuto-assigned + paged · MFA notification
Verification lead → re-open Vmin corner regressionVela queued 4-hour priority run
Customer success → draft response with known factsPre-filled response template attached
VP Eng → escalation notification with full contextSent · awaiting decision on customer hold
From customer escalation to fully-routed triage in 4 minutes. Posi correlated the lab signature against design intent, RTL history, and prior similar bugs — surfacing the most likely root cause before the first engineer opened the bug. Multi-agent: Posi · Aria · Vela.
Cluster 03 · Learn
Reconstruct what happened. Defend every decision.
"We missed tape-out by 17 days. Why? Don't tell me 'verification was late.'"
T-104 · Jan 8
Vela flagged DDR5 controller coverage trajectory as off-trend. Routed to verification lead. Acknowledged.
T-87 · Jan 25
Verification lead requested 2 additional engineers. Decision deferred to next quarterly capacity review.
T-62 · Feb 19
Phin flagged downstream timing impact: insufficient verification confidence on DDR5 to lock floorplan. Surfaced to program lead.
T-41 · Mar 12
First missed milestone: DDR5 freeze deferred 2 weeks. Cascade to PD signoff predicted by Phin within 1 hour. Not escalated to VP Eng.
T-28 · Mar 25
Sora integration regression failed top-level. Engineers worked weekends. PD ECO loop began.
T-0 · Apr 22
Tape-out missed by 17 days. Final cause attributed to "verification + PD interaction." Truth: traceable to a deferred capacity decision 87 days before tape-out.
RCA Complete · 4 Minutes · Replaces Six-Week Committee
Root cause: an unescalated capacity decision, not a verification methodology failure. Tymeline reconstructed the decision chain across 104 days, named the deferral, surfaced the unescalated cascade, and generated three process change recommendations. The next program won't repeat this — because the pattern is now in Vela's and Orin's memory.
"Auditor needs the full decision chain on the safety-critical block. By Friday."
Block scope
Functional Safety Island
v3.2.1 · 47 sub-blocks
Decisions in scope
2,847 decisions
across 22-month program
Provenance status
100% anchored
tamper-evident · verified
Approver chain
All 2,847 decisions
name-attributed · MFA-verified
Source data refs
14,203 retrievals
each linked to source
Export format
SIEM-ready
Splunk · Sentinel · CSV
Audit packet generated in 9 minutes — not 9 weeks. Every decision touching the safety-critical block, with full provenance: which agent retrieved which data, which reasoning chain, which policy was consulted, who approved. Reproducible. Defensible. Tamper-evident at the cryptographic layer. The audit is no longer the project — the audit is the artifact you already have.
Under Every Scenario · The Decision Trace

Every answer Tymeline gives you arrives with a receipt.

The eight scenarios above all produce the same artifact underneath: a Structured Decision Record. Cryptographically anchored. Tamper-evident. Reproducible. This is what makes the difference between an AI that's interesting to play with and an AI that can sit inside a regulated semiconductor program. Built against the runtime governance pattern emerging from Forrester AEGIS, Microsoft's Cloud Adoption Framework, Bain's three-layer agentic platform, and Oracle's runtime governance framework.

01

Agent Identity

Every AI Employee operates as a named service identity registered in your IdP. Per-agent scoped credentials. Least-privilege tool entitlement.

02

Decision Provenance

Cryptographic hash anchored on Tymeline ID's blockchain layer. Reproducible, replay-able, immutable across the program lifecycle.

03

Human-in-the-Loop

Per-action approval thresholds. Named approvers per decision class. Hard stops. Emergency suspend on demand.

04

SIEM Export

Native export to Splunk, Sentinel, Chronicle. Decision records flow into your existing security operations pipeline as structured events.

SAMPLE RECORDScenario 1.1 above
DECISION IDDEC-7F3A9B
STATUSSIGNED · ANCHORED
09:42:11
INTENT
VP Eng directive: tape-out T-19, hold WNS > 0, no scope creep · approver: J. Chen
09:42:14
CONTEXT · PHIN
Retrieved: 11 day slip on DDR5 PHY timing closure · source: Cadence Innovus session 4471
09:42:14
CONTEXT · VELA
Retrieved: UVM coverage 41% on DDR5 controller · source: regression DB run 9281
09:42:15
MODEL
Inference invoked: Tymeline-managed LLM via VPC endpoint · 12,847 input tokens · 1,203 output tokens
09:42:16
REASONING
Cross-agent analysis: cascade risk to top-level integration, +14 day slip probability 0.87
09:42:16
POLICY
Engineer reallocation requires approval · approver: VP Engineering · routed
09:51:02
APPROVAL
APPROVED by VP Eng · MFA verified · 2 senior PD engineers reallocated
09:51:02
PROVENANCE
Hash sha256:7f3a9b… anchored · all sources, reasoning, policy, approval preserved · exported to Splunk
The Hybrid Team Economics

$17M+ a year recovered on a 50-engineer design center. Math, not marketing.

A hybrid team is your engineers, plus AI Employees sharing context with them — handling the coordination and synthesis work that currently steals 30% of every engineer's time. Below is the side-by-side math with three tiers of value: conservative operational savings (the floor no one can argue with), market value (revenue captured earlier from faster time-to-market), and risk avoidance (annualized re-spin protection). Every number is grounded in published benchmarks — McKinsey, Glassdoor, AWS, Softweb, EDA vendor data. Adjust the inputs to your own team; the structure of the math holds.

Baseline Inputs · Documented Industry Benchmarks
$149K
Avg US semi design engineer salary
GLASSDOOR · MAR 2026
~$210K
Fully loaded (1.4× incl. benefits, equity, overhead)
CONSERVATIVE EST.
35%
AI productivity gain · formal verification
EDA VENDOR COPILOT EARLY ADOPTERS · 2025
2×
Junior engineer uplift with GenAI assist
AWS · INFOSYS · 2024–2025
864 days
Engineer-days per leading-node 5nm SoC
MCKINSEY VIA AWS · 2024
$540M
Total dev cost · 5nm SoC (industry avg)
MCKINSEY · 2024
12 → 8 mo
Design cycle compression with AI tooling
SOFTWEB / INFOSYS · 2025
~30%
Engineer time on coordination & status
INDUSTRY ESTIMATE · CONSERVATIVE
Worked Example · 50-Engineer Design Center · 1 Year
Without Tymeline  vs  With Tymeline (Hybrid)

Without Tymeline · Humans Only

Engineers 50
Fully loaded cost / engineer / year $210,000
Total annual labor $10.5M
Time on coordination & status (~30%) $3.15M lost
Time on actual engineering (~70%) $7.35M
EDA license cost (typical pool) $2.0M
Total run-rate $12.5M
Coordination tax: $3.15M / yr leaking out
Re-spin risk exposure: $15M+ / event
Time-to-market lag vs. AI-tooled peers: ~4 mo / program
Engineer attrition (industry avg ~15%): ~$2.2M / yr

With Tymeline · Hybrid Team

Engineers (no headcount cut) 50
AI Employees (one per silicon discipline, 24/7) 7
Tymeline platform + AI Employees $0.21M
Coordination overhead reduced 50% −$1.58M
Verification productivity +35% on 15 eng −$1.10M
Cross-team productivity +10% on other 35 eng −$0.74M
EDA license utilization +30% efficiency −$0.60M
Engineer retention improvement (~30%) −$0.66M
Time-to-market value (1 quarter early) +$5.0M
Re-spin avoidance (annualized risk) +$7.5M
Total annual value captured $17.0M+ / yr
Engineering capacity recovered: ~12 FTE-years / yr
Total Value Captured · 50-Engineer Design Center · Year One
$17M+ / yr · ~135% ROI on $12.5M baseline
More than the entire team's labor cost, captured back as value. The math compounds across three layers — operational savings ($4.7M from coordination, productivity, EDA, retention), market value ($5M from earlier revenue capture), risk avoidance ($7.5M annualized re-spin protection). Each layer is grounded in published benchmarks. Payback inside 6 days. ROI multiple ~80×.
The Real Story · What "7 AI Employees" Actually Means
7 AI Employees — one embedded in every silicon discipline — running 24/7, absorbing the coordination work that currently consumes ~30% of every engineer’s day.
Aria with the 10-person RTL team. Vela with the 16-person verification team. Phin with the 10-person PD team. Dax with DFT. Ana with analog. Sora with integration. Orin with eng-ops. Each one is the named teammate for that team’s work — not a generic chatbot, not a sprinkle. That’s ~12 FTE-years of recovered engineering focus per year, at zero headcount cost. Your 50 engineers stay 50 engineers. They just stop spending afternoons on status reports, regression triage, and Jira archaeology, and start spending them on actual silicon.
Conservative ROI · The Floor No One Can Argue With
$3.0M / yr · ~14× ROI
If you only count the operational savings (coordination overhead reduction, documented verification productivity gains, EDA license efficiency) and ignore everything else — time-to-market, re-spin avoidance, retention — the floor is still $3M+ per year. Payback under 4 weeks. This is the number to use if a CFO challenges every assumption.
Re-Spin Avoidance · Single Event Upside
$15M+ avoided per re-spin prevented
A single coordination-traceable re-spin avoided pays for the platform across the entire engineering org for the next 5+ years. Leading-node masks alone exceed $5–30M. The annualized number above ($7.5M) assumes a ~50% probability of one re-spin per year on a 50-engineer team — historically conservative.
How to read the math
Every number above traces to a documented benchmark — Glassdoor 2026 salary data, McKinsey design economics, EDA vendor copilot productivity reporting, AWS/Infosys junior uplift studies, Softweb design cycle analysis, plus standard industry attrition and re-spin frequency. The conservative tier ($3M) excludes time-to-market value, re-spin avoidance, retention, and cross-team productivity uplift — all real but harder to put a single number on. The total value tier ($17M+) includes them with conservative coefficients. Adjust any input to your design center; the structure of the math holds.

Three hybrid team models. Pick the one that matches your org.

Hybrid teams are not a single shape. Below are the three operating models semiconductor design centers most commonly deploy with Tymeline. Numbers above scale linearly — a 200-engineer design center captures roughly $65M+ / yr in total value; a 600-engineer org captures $180M+ / yr. The conservative operational floor scales the same way: ~$12M/yr for 200 engineers, ~$36M/yr for 600.

Model 01 · Embedded

One AI Employee per silicon team

Each functional team (RTL, DV, DFT, PD, etc.) gets one named AI Employee embedded as a permanent member. Highest context depth. Best for large established programs.

RATIO · 1 AI : 15–25 ENGINEERS
Model 02 · Pooled

Cross-team AI Employee pool

A pool of AI Employees serves multiple teams across a design center. Flexibility-first. Best for matrix orgs and centralized design engineering services.

RATIO · 1 AI : 30–40 ENGINEERS
Model 03 · Leadership-Only

AI Employee per program / VP

One senior AI Employee dedicated to each program lead or VP. Strategic intelligence and cross-program oversight. Smallest deployment footprint, fastest start.

RATIO · 1 AI : 1 LEADER
Data Ownership · The Tymeline ID Architecture

Tymeline does not own your data. You do. Always. Mathematically.

Tymeline ID is the cryptographic backbone of how data flows in and out of the platform. Every record — engineer credential, project signal, decision trace — is authenticated at source, encrypted, and anchored on a decentralized public blockchain. The data subject (your engineer, your team, your company) holds the access key. Tymeline holds nothing.

How company data ownership actually works.

For your engineering organization, this means three things that no other AI platform offers in writing: tamper-proof history, sovereign access control, and zero training capture. The architecture is not a policy promise — it is enforced cryptographically.

  • Source-verified, source-owned.Every record is verified at the source organization (your design center, your HR system, your code repo) and signed before persistence. The source organization remains the authoritative owner.
  • Consent-gated access.Every read against your data requires explicit access grant. Grants are time-bounded, scope-bounded, and revocable. Revocation propagates across caches and replicas.
  • Tamper-evident provenance.Records are hashed and anchored on a decentralized public blockchain. A modified record fails verification on next read. Audit reconstruction is mathematical, not procedural.
  • No training capture, contractually.Your engineering data is never used to train Tymeline's base models or any third-party foundation model. Per-tenant fine-tunes (if used) are cryptographically isolated. DPA enforces this.
  • Right-to-erasure cascades.GDPR Article 17 erasure requests cascade through agent memory, retrieval caches, decision archives, and backups. Provable deletion, not "we'll get to it."
From the Tymeline ID architecture
"We don't own your data. You are in charge of your data and in full control when sharing it. Your data is 100% encrypted and on decentralized infrastructure."
SOURCE · tymeline.id
100%
Encrypted at rest
0
Records used to train models
∞
Audit replay window
Every Silicon Team. Every Use Case.

Built for the way silicon teams actually work.

Ten silicon engineering teams. Ten named AI Employees, each with deep domain context for the work that team owns. From front-end RTL design through post-silicon validation and engineering operations — every team in the design center is covered, with a defined pain it removes and use cases grounded in real silicon workflow.

10Teams covered
10AI Employees
40+Use cases pre-built
RTL → Post-SiEnd-to-end coverage
01
RTL Design
Front-end microarchitecture · Verilog / SystemVerilog · spec-to-RTL
Hybrid Team · 17 humans + Aria
How the team works together

Your RTL designers stay focused on logic. Aria handles the spec-tracking, CDC triage, and interface coordination that currently steals their afternoons.

What Aria does, so the humans don’t have to
01
Spec drift detection
Continuously diffs RTL against architectural spec; flags divergence before DV.
02
CDC & lint triage
Auto-categorizes findings by severity and likely owner; routes only what needs review.
03
Interface freeze tracking
Live status of every block-to-block interface contract; alerts on change.
04
Review packet prep
Assembles change summary, coverage delta, risk flags ahead of every design review.
02
Design Verification
UVM testbenches · constrained random · coverage closure · formal
Hybrid Team · 26 humans + Vela
How the team works together

Your verification leads stay focused on closure strategy. Vela handles the regression synthesis, flake hunting, and coverage forecasting they currently lose nights to.

What Vela does, so the humans don’t have to
01
Coverage closure forecast
Predicts coverage trajectory per block; flags blocks unlikely to close on time.
02
Flaky test isolation
Identifies intermittent failures across runs; quarantines and assigns ownership.
03
Bug aging analysis
Tracks defect lifetime and recurrence; surfaces blocks accumulating tech debt.
04
Testbench reuse mapping
Detects reusable verification IP across projects to avoid duplicate testbench builds.
03
Design for Test (DFT)
Scan insertion · ATPG · MBIST · boundary scan · test coverage
Hybrid Team · 7 humans + Dax
How the team works together

Your DFT engineers stay at the cross-team seam where they belong. Dax pulls together the RTL, PD, and ATE context they currently chase across three orgs.

What Dax does, so the humans don’t have to
01
Testability gap detection
Reads RTL and floorplan; flags blocks where scan insertion will hit congestion.
02
ATPG budget tracking
Forecasts pattern count against tester time budget; alerts on overrun.
03
MBIST coordination
Tracks memory inventory, MBIST controller assignments, and test wrapper status.
04
Bring-up readiness
Builds test program assembly status report ahead of first silicon arrival.
04
Physical Design / Implementation
Synthesis to GDSII · floorplanning · place & route · timing closure
Hybrid Team · 18 humans + Phin
How the team works together

Your PD engineers stay on timing closure and ECO craft. Phin handles the cross-block synthesis, license arbitration, and leadership reporting that currently fragments their day.

What Phin does, so the humans don’t have to
01
Timing closure forecast
Reads STA reports across blocks; predicts WNS/TNS trajectory and signoff readiness.
02
ECO impact analysis
When an ECO lands, computes downstream impact on power, timing, area, and DRC.
03
Congestion triage
Surfaces blocks at risk of routing failure before P&R consumes an overnight run.
04
License-aware scheduling
Coordinates EDA tool license usage against critical-path tasks; prevents queue waits.
05
Analog & Mixed-Signal
PHYs · PLLs · SerDes · ADC/DAC · bandgap · custom layout
Hybrid Team · 12 humans + Ana
How the team works together

Your analog engineers stay on the craft. Ana captures the tribal knowledge — PVT corners, characterization, silicon correlation — that currently lives in spreadsheets and email.

What Ana does, so the humans don’t have to
01
PVT closure tracking
Aggregates corner sweep results across blocks; flags corners not yet covered.
02
Characterization lineage
Versioned silicon correlation per IP block, per node, per foundry process variant.
03
IP reuse recommendations
Surfaces analog IP from prior projects matching current block requirements.
04
Lab-to-sim correlation
Correlates lab measurement data against simulation results during silicon bring-up.
06
SoC Integration
Top-level integration · IP wrapping · NoC · subsystem assembly
Hybrid Team · 8 humans + Sora
How the team works together

Your integration engineers stay on assembly and bring-up. Sora moves their problem-discovery left — catching version skew and interface drift before the hierarchy comes together.

What Sora does, so the humans don’t have to
01
IP version reconciliation
Tracks every IP block version against vendor delivery and integration branch.
02
Interface contract validation
Verifies block-to-block interfaces against published contracts; flags drift.
03
Clock & reset domain map
Live CDC/RDC topology across the SoC; surfaces conflicts before silicon bugs.
04
Top-level regression triage
Routes integration failures to the responsible block owner with reproduction context.
07
VIP & Methodology
Verification IP · UVM frameworks · methodology · CI/CD for HW
Hybrid Team · 6 humans + Mira
How the team works together

Your methodology team stays focused on building VIP and CI infrastructure. Mira tracks adoption, drift, and pipeline health across the org so governance isn't just hope.

What Mira does, so the humans don’t have to
01
VIP version compliance
Tracks which projects are on which VIP version; flags forks and drift.
02
Methodology lint
Continuously scans testbenches for guideline violations; routes findings to owners.
03
CI pipeline health
Monitors regression infrastructure for queue saturation, flake rate, contention.
04
Reusable asset surfacing
Identifies reusable VIP across business units to prevent reinvention.
08
Packaging & Substrate
Bump map · package design · 2.5D/3D integration · thermal · SI
Hybrid Team · 9 humans + Pax
How the team works together

Your package team stays on substrate, thermal, and OSAT coordination. Pax catches the PD bump map changes that ripple into substrate before they cost weeks of substrate spin.

What Pax does, so the humans don’t have to
01
Bump map propagation
Detects PD bump changes; computes impact on substrate routing and BGA assignment.
02
Thermal & SI risk surfacing
Flags blocks with power profile changes affecting package thermal envelope.
03
OSAT readiness tracking
Live readiness state for each OSAT deliverable: GDS, bump map, package, thermal.
04
2.5D/3D dependency map
For chiplet designs, tracks die-to-die contracts and silicon interposer dependencies.
09
Post-Silicon Validation
Bring-up · characterization · system validation · silicon debug
Hybrid Team · 14 humans + Posi
How the team works together

Your post-silicon engineers stay in the lab. Posi correlates the lab data, simulation logs, and design intent across teams in real time, so triage starts with evidence, not guesses.

What Posi does, so the humans don’t have to
01
Bug root-cause routing
Correlates silicon bug signature against design intent and prior similar bugs.
02
Bring-up dashboard
Live status across boards, test cases, and engineers — visible without status meetings.
03
Lab-to-sim correlation
Compares lab measurements against simulation; surfaces deltas as silicon risk signals.
04
Customer sample tracking
State of every engineering sample shipped, the customer using it, open issues per sample.
10
Engineering Operations & PMO
Program management · workforce planning · EDA license ops
Hybrid Team · 5 humans + Orin
How the team works together

Your Eng-Ops team stays on the strategic decisions — capacity, licensing, vendor management. Orin replaces the spreadsheet archaeology with a live, role-aware capacity and license picture.

What Orin does, so the humans don’t have to
01
Verified capacity map
Live, role-aware visibility into who is qualified for what — and currently available.
02
EDA license utilization
Real-time license usage mapped against critical-path tasks; flags wasted seats.
03
Vendor IP delivery tracking
Monitors third-party IP and PDK deliverables against contractual commitments.
04
Burnout & retention signals
Detects sustained over-allocation patterns predictive of attrition.
The Full Tymeline Platform

Everything Tymeline does — applied to silicon engineering.

The semiconductor capabilities sit on top of Tymeline's full enterprise platform. The same engine your design center uses for tape-out command runs your finance, HR, and operational analytics — with the same governance, the same data ownership, the same audit trail.

Project Hub

AI-Driven Project Intelligence

Real-time dashboards across every active program. Predictive risk detection. Authentic analytics built on verified historical data. Auto-generated goal roadmaps tailored per engineer.

Talent Hub

Workforce Intelligence System

Continuous performance management replacing annual reviews. Skill-based upskilling recommendations. Hiring forecasts aligned to project demand. Burnout and attrition signals.

Orb

Executive Strategic Visibility

C-suite dashboard surfacing hidden inefficiencies, resource leaks, and underperformance. Gap analysis across departments. Predictive insight into financial, human, and material risk.

Via

AI Mission Intelligence Interface

Conversational interface to ask anything of your live program data. Role-aware answers for the CTO, VP Eng, or block lead. Embedded in every Hub.

HyperOrg

Autonomous Organizational Intelligence

Tymeline's AOI framework. Strategy evolves based on internal performance and external signals. Simulation-driven planning with digital twins. Open-source model foundation.

Tymeline ID

Verified, Portable Engineer Identity

Every engineer gets a Tymeline ID — a verified, blockchain-anchored profile of skills, project history, and performance. Engineer-owned, organization-verified, fully portable.

Finance Hub

Project-Level Financial Visibility

Budget utilization, cost forecasting, and ROI per program. Connects to ERP and accounting systems. Real-time monitoring of expense, profitability, and resource cost — by tape-out program.

Marketing & Add-On Hubs

Domain-Specific Operational Hubs

Marketing campaign performance, FieldForce for on-site service teams, Retail Hub for store operations. The platform expands beyond engineering as your needs grow.

Integrations

200+ Native Connectors

Jira, Linear, Asana, GitHub, GitLab, Perforce, Confluence, Slack, Teams, Okta, Entra ID, and 200+ more. Custom connectors for internal tools. Five-minute onboarding.

Model Flexibility · Managed by Tymeline

The model layer is Tymeline's problem. Not yours.

Tymeline manages the underlying model infrastructure for every AI Employee — selection, hosting, versioning, swap, kill switch. You don't pick the LLM. You don't run inference. You don't pay token bills directly. What you choose is the deployment posture (SaaS, VPC, or air-gapped on-premise) and the compliance envelope. Tymeline handles everything below that line.

Default · Managed
Frontier Tier

For most programs. Tymeline-managed inference on frontier closed-weight models. Long-context reasoning, agent orchestration, code understanding. Zero infrastructure for the customer.

SaaS
Hyperscaler VPC
Sensitive · Managed
VPC-Hosted

For IP-sensitive programs. Tymeline-managed inference inside your cloud environment. Your network, your KMS, your IAM. Tymeline still runs the model layer.

VPC
KMS-bound
Restricted · Managed
Open-Weight Self-Host

For ITAR or foundry-restricted programs. Tymeline ships and operates open-weight models on your air-gapped infrastructure. No external network egress. Tymeline-supported.

On-Prem
Air-Gap
Optional · Customer Choice
Custom Model

If your security team requires a specific approved model, Tymeline integrates it as the inference layer behind every AI Employee — no agent re-architecture required.

By request

Tymeline operates the model layer. Customer-facing surface is the AI Employee. No prompt engineering required. No token cost management required. No "build your own RAG pipeline" required.

Built for the IP-Sensitive Enterprise

Three deployment models. One governance posture.

Semiconductor design data is among the most sensitive intellectual property in the world. Tymeline's deployment options are designed for organizations operating under export control regimes (EAR, ITAR), foundry NDAs, and customer confidentiality agreements simultaneously.

Option 01
Tymeline Cloud (SaaS)

Multi-tenant SaaS in your chosen region. SOC 2 Type II, ISO 27001, GDPR. Fastest path to production for non-export-controlled programs.

Option 02
Dedicated VPC

Single-tenant deployment inside your own cloud environment (any major hyperscaler). Your network, your KMS, your IAM. All data plane traffic stays in your VPC.

Option 03
On-Premise / Air-Gap

Fully air-gapped deployment for ITAR-controlled or foundry-restricted programs. Open-weight model layer hosted on your infrastructure. Zero external network egress.

Pre-Answered for IT, Security & Procurement

The questions your reviewers are about to ask.

Will our RTL, netlists, GDS, or any design IP leave our environment?
No. Tymeline's AI Employees operate against metadata and structured engineering signals (Jira tickets, regression results, license utilization, status from EDA flows). Source RTL, netlists, and physical design databases stay in your existing repositories. For air-gap deployments, no data of any kind crosses your network boundary.
Are our prompts and engineering data used to train models?
No. Customer interactions are not used to train Tymeline's base models or any third-party foundation model. Per-tenant fine-tunes (where used) are cryptographically isolated. Tymeline does not own your data — Tymeline ID's architecture enforces this contractually and cryptographically.
Which AI model runs underneath, and can we control it?
You choose. Frontier closed-weight LLMs (via vendor API or hyperscaler VPC) or open-weight models (Mistral, LLaMA family) self-hosted on your infrastructure. Per-AI-Employee model selection — use a frontier model for long-context reasoning, a smaller model for routine triage, and a fully air-gapped open-weight model for ITAR programs. Switch models without re-architecting your deployment.
How do we audit what an AI Employee did and why?
Every consequential action produces a Structured Decision Record: the intent it was operating under, the context it retrieved (with source attribution), the model invoked, the reasoning chain, the policies consulted, and any human approvals. Records are cryptographically anchored on Tymeline ID's blockchain layer and tamper-evident, exportable to your SIEM (Splunk, Sentinel, Chronicle).
What about export control (EAR / ITAR)?
Project tags configurable to mark export-controlled programs, with role-based access enforced against US Person status from your IdP. All access to controlled technical data is logged with provenance. For ITAR programs, the air-gapped on-premise deployment with self-hosted open-weight models removes any cross-border AI inference question entirely.
What identity, access, and SSO do you support?
SAML 2.0 and OIDC SSO with Okta, Microsoft Entra ID, Ping, Auth0, and most enterprise IdPs. SCIM 2.0 for user/group provisioning. RBAC and ABAC enforcement. Per-agent service identities with scoped credentials. MFA required for all human and approval-gated agent actions.
How does pricing work, and what's the budget category?
Tymeline is licensed per-seat for human users; AI Employees are included as part of the platform. Most semiconductor customers categorize this as engineering productivity / program management infrastructure, not a separate AI line item. For a 50-engineer design center, total platform cost typically runs ~$150–200K annually — paid back inside 3 weeks against the documented productivity gains above. Larger orgs scale linearly: a 200-engineer center is ~$0.6–0.8M; a 600-engineer org is ~$1.8M+.
What's a realistic pilot scope and timeline?
A typical semiconductor pilot covers one design team or business line (anywhere from 50 to 600 engineers), runs for 8–12 weeks, and targets a single tape-out program already in flight. Onboarding is 5 days for SaaS, 2–3 weeks for VPC, 4–6 weeks for air-gap. Success criteria agreed upfront against measurable outcomes: planning overhead, milestone slip rate, retention.
What if an AI Employee makes a wrong call?
No AI Employee acts on consequential decisions without human approval — that's policy, enforced at runtime. For approved actions, the full decision trace makes any mistake traceable to its inputs. You can roll back, suspend, or decommission any AI Employee version with one command; memory and decision history preserved for audit.

The compliance posture your reviewers expect.

Independent third-party attested. Renewed annually.

SOC 2 Type II
ISO 27001
GDPR
SAML / OIDC
SCIM 2.0

Your tape-out is the mission. See Tymeline command it.

See Tymeline against your real program — not a generic demo. We'll arrive with a security questionnaire response, an architecture document, and a deployment plan tailored to your design center.

Book a Semiconductor Briefing → Talk to Iva