Run every AI task on the most cost-efficient model that can actually do it.
Heimdall routes each coding task to the most cost-efficient capable model and makes every result prove itself: code changes must pass the compiler; answers must cite file:line sources that Heimdall mechanically verifies against your repo. Escalation happens only when the evidence fails — with DLP, budgets, and audit on every request.
The watchman's promise: as much control as each tool allows — full visibility regardless. Nothing crosses the gate without Heimdall seeing it.
Every company is becoming an AI company. Almost none can run AI like infrastructure.
When large IT-services firms tell employees not to use AI for small tasks, it isn't anti-AI — it's a company with no way to control when and which AI gets used. Today the only choices are both bad:
Ban AI for small tasks
You claw back the invoice — and lose most of the productivity the tools were bought for.
Let everyone use premium AI
Cursor, Claude, GPT on everything — the bill explodes, and most of it is overkill for the task.
Nobody routes intelligently — small task to a low-cost model, hard task to the best model, automatically, with quality protected and every request governed. That gap is Heimdall.
Don't predict which model a task needs. Try low-cost, then prove it.
Prediction is unreliable — we measured it, and so has everyone else. So Heimdall runs the lowest-cost loop-capable model first, demands evidence, and only climbs the price ladder when the evidence fails. The gate is deterministic checks — never an AI judging an AI.
Run the most cost-efficient capable model
An agentic tool-loop lets the model grep, read, and iterate to gather the exact code context it needs — starting on the lowest-cost model that can actually drive that loop.
Demand evidence, verify it mechanically
Code changes must parse, type-check, and build. Answers must cite file:line sources — Heimdall verifies every cited file and symbol actually exists in your repo, and a second independent sample must ground in the same code. No LLM judges.
Ship, or escalate one tier
Pass: done, paid pennies. Fail: Heimdall recovers and escalates one tier up, then verifies again. Most tasks stay low-cost; only the hard tail reaches premium.
Follow one request from prompt to shipped — through the gate.
This is the exact path every task takes. Governance happens before any provider sees your data; the model is chosen by difficulty; the cascade climbs only when a verify check fails. Watch it run.
One governed control plane between your developers and every AI provider.
Routing is the mechanism. The product is everything an enterprise needs to actually deploy AI at scale — and the memory that makes it better and less costly over time.
Intelligent routing
Each task lands on the most cost-efficient capable model, with the routing decision, difficulty, per-request cost, and projected savings shown for every call.
Shared org memory
One memory store shared across models — and, opt-in per project, across the org. Tell Claude something; ask GPT and it knows, because the knowledge lives in Heimdall, not the model. Every verified task enriches the next one.
Governance, built in
DLP secret-scanning, per-team budgets and token caps, and allow / deny / limit policies evaluated at the gateway before each request — with an append-only audit trail.
Outcomes you can prove, not just spend
Cross-provider spend in one schema, waste detection (idle seats, right-sizing, redundant tools), and — via the AI Ledger — cost per shipped, production-healthy story, not just token counts. See how →
Lives in VS Code
The full surface inside the editor: sends the exact code spans a task needs — not whole files — with the model, tokens, cost, and % saved shown on every turn, and native diff → Apply.
Runs in your account
Heimdall is not a reseller. Traffic runs through your own AWS Bedrock account; keys are vaulted and never sent to clients. No-log mode, SOC 2 & GDPR on the roadmap.
As much control as each tool allows. Full visibility, regardless.
Closed tools can't be governed like an API you proxy — so we don't pretend otherwise. Every AI surface is tagged by exactly how much control is real. That honesty is what a CISO can actually deploy.
Traffic proxied through the gateway
Hard budget and DLP enforcement before the request reaches a provider. Full control — for the API models you route.
Access & seats provisioned
Usage pulled from the tool's admin API. You control who has a seat and see what they spend, even when you can't proxy the traffic.
Visibility only
For the most closed tools: usage and cost surfaced in one place, so nothing is invisible — even where enforcement isn't possible.
Three levels, one schema. Nobody else has every provider's cost, usage, and policy in a single normalized store.
Measured on a 1,100-file production repo. Graded against verified ground truth.
Two benchmarks on a 1,105-file production repo: 31 hard questions for model economics, and a sealed 16-question held-out exam — brand-new questions, no tuning possible, one attempt — for the evidence-gated router itself.
Everyone measures AI activity. Nobody can prove AI outcomes.
Engineering-analytics tools see commits and tickets but not what was AI-made, what it cost, or whether it shipped and stayed healthy. Billing dashboards see spend but no outcomes. Heimdall is in the request path and joins that spend to the work it produced — so it can put cost per shipped, production-healthy story on one screen. Team-level by design, never ranking individuals.
Every request carries its work key
The extension stamps each call with the session and task it belongs to, so spend is joinable to outcomes from the moment it happens — not reconstructed after the fact.
Spend joins to commits, stories & releases
The AI Ledger connects dollars → commits → shipped stories → releases via your CI/CD and issue tracker. Because we own both ends of the chain, the join is real — not a guess a dashboard makes from the outside.
Cost per shipped, production-healthy story
Release health and incidents fold back in, so a story only counts once it shipped and stayed up. The CFO number stops being "tokens spent" and becomes "dollars per outcome that held in production."
Both ends of the chain. In the request path for cost, joined to CI/CD and production for outcomes — the one place they meet. And it's team-level by design, which is what makes it sellable past a works council.
The router is copyable. Your org's context is not.
AI is arriving bottom-up, tool by tool
Teams adopt Cursor, Claude, GPT, and agents faster than any company can govern — with no single place to manage who uses what, at what cost, under what policy.
Every routing outcome is logged and learned
Every one of your org's routing outcomes — accepted evidence, escalations, failures — teaches the router; better context lets an even lower-cost model succeed next time. That compounds with use.
Leaving means losing years of context
When Heimdall becomes where your engineering knowledge accumulates, it's the durable moat a competitor can't copy — the same job Okta did for SaaS identity.
We own the request path and the outcome
Analytics vendors see commits but not what was AI-made or what it cost; billing dashboards see spend but no outcomes. We're in the request path and joined to shipped, production-healthy work — so only we can put cost-per-outcome on one screen.
“Everyone sells AI usage. We're building AI accountability.”
Same quality, a fraction of the cost — with shared org memory as the moat we build next.
Each role gets only the AI it needs — bought at volume, on one governed bill.
The customer pays less than buying tools separately, because API usage routes to the most cost-efficient model that works. We don't make Cursor cheaper than Cursor — we make the whole stack cost less.
Full-stack AI seat
For engineers shipping code every day.
- Cursor at volume + routed Claude / GPT pool
- VS Code extension + shared memory
- Full governance & audit
Routed API seat
For everyone else who works with AI.
- Routed GPT / Gemini / Claude pool
- No premium seat to pay for
- Full governance & audit
Creative AI seat
For design and product teams.
- Figma AI at volume + image / chat pool
- Shared memory across tools
- Full governance & audit
Role-based bundles priced per seat on your mix and volume. We keep a healthy margin; you still pay less than separate tools.
The hard part isn't another chat box near code. It's deciding who can do what, what data can leave, what a task should cost, and whether the result is trustworthy.
engineering systems
Background in identity & access control — direct founder-market fit around policy, provisioning, and developer adoption.
& technical execution
Focused on making the control-plane thesis work as a daily tool engineering teams actually want to use.
Let developers use AI. Just route it, verify it, and govern it.
A 30-minute walkthrough on your own codebase: watch the cascade route, verify, and escalate — with cost and policy on every request.