Every team is now coding with AI. Optimem lets you push that adoption hard — while keeping the spend cost-optimal and compliant, with full visibility and zero change to how any developer works.
~90% of enterprise developers now use AI coding tools weekly. Spend, model choice, and what context leaves the building are largely ungoverned.
Provider bills climb with every agent loop. Which team, which project, which model — hard to see, harder to control.
A critical-security team and a prototyping team get the same unbounded access. Model choice, budgets, and DLP are all-or-nothing.
Claude Code, Cursor, Cline, Copilot, Codex, Aider — each team picks its own. Governance can't be tool-by-tool.
The tension for a VP: you want maximum AI adoption for velocity — but you're accountable for the spend and the risk that comes with it. Today you usually have to trade one for the other.
Optimem is a governance plane: it sits between your developers' AI tools and the AI providers (Anthropic, OpenAI, Google, Bedrock, Vertex…). On every request it resolves the right policy for that team, enforces it, optimizes the call, meters it, then proxies to the provider using your own provider keys.
Which models a team may use, budget caps, security/DLP, rate limits — resolved per team, per repo, per group.
Compress runaway context and route to right-sized models — cutting token cost on calls that would otherwise overpay.
The developer experience is unchanged. They point their tool at Optimem's URL once — everything else is invisible to them.
The developer sets their tool's base URL to Optimem instead of the provider. From then on, every request flows through the plane:
The response streams straight back, untouched. Optimem never rewrites what the model sends — it copies the stream through byte-for-byte, so latency and behavior are the provider's. It only observes enough to meter and attribute.
A Policy Bundle is a named, reusable set of rules — like a managed ruleset. You (or your admins) assign a bundle to a team, and every request from that team is governed by it.
Which models & tiers are allowed or blocked.
When to compress context to control cost.
Data-loss hooks and secret masking.
Soft/hard spend limits with defined behavior.
Why a VP cares: instead of configuring dozens of knobs per team, you pick the right bundle for each team based on its criticality, deadlines, and security needs. Optimem ships proven builtin bundles; your admins can clone and tune them, or leave a policy off entirely. Optimem lowers the expertise bar — it never removes your control.
Bundles are assigned per group and inherited down the org → department → team → repo hierarchy. A child can only tighten what a parent set — never loosen it. Security holds by construction.
If a bundle assignment ever goes missing, resolution fails closed — it falls back to your org default with an alert, never silently to an ungoverned passthrough. Governance is never accidentally dropped.
Governance is the category. Optimization is what makes Optimem save money, not just report it. Each optimization is metered per action and priced below the value it produces — so it's self-funding, not a new cost line.
Shrinks bloated conversation context before it re-bills at full price.
Sends simple work to a right-sized, cheaper model tier automatically.
Both are transparent to developers and validated on 8,000+ real agentic coding sessions.
AI coding tools re-send the whole conversation history every turn. Providers cache it briefly — but when that cache expires, the entire history re-bills at the higher write price. Optimem detects that moment and merges the history into a compact summary, so the next turn pays for a fraction of the tokens.
It's additive, not competing. It works alongside the provider's own caching, and extends across providers — including long-lived OpenAI/Gemini sessions via a spend-per-turn budget you set. No fee is ever charged when the algorithm ran but changed nothing.
Not every request needs the most expensive frontier model. Optimem reads a live complexity signal on each request and, when the work is below an Optimem-validated threshold, substitutes a lower-cost tier — but only if the team's bundle permits it. It's deterministic threshold routing, not opaque ML.
Thresholds live inside the bundle. A critical team's bundle can forbid routing entirely; a prototyping team's can route aggressively. Same engine, per-group policy.
The complexity signal is already live and metered today — so even before a team turns routing on, your dashboard shows exactly how often frontier models are being used for work that didn't need them.
Optimem proxies with your org-owned provider keys. It never resells tokens; provider spend stays yours. Can run as SaaS or fully inside your VPC with one env-var change.
A secret scrubber runs on every log path. Keys never appear in logs, ever.
Every query is org-scoped and row-level enforced. No cross-org data leakage, by construction.
A payment issue locks the dashboard and emails you — it never cuts off your developers' traffic. Velocity is protected.
Optimem meters every request and turns it into reports that let you decide which bundle each team should run — grounded in expected ROI, not guesswork.
Framing for the budget conversation: the governance fee is about the cost of one single-tool seat (like a Copilot Business seat) — but it governs every AI tool a developer uses, across every provider. And the optimization layer is designed to return more than its own fee. The net effect on the AI line should be flat-to-down, with control added.
This is the unlock: the usual reason governance stalls is that it slows developers down. Optimem adds control without touching the developer's loop — so you can say yes to more AI adoption, not less.
Optimem's ongoing job is to research the field, validate new techniques, and ship each as a new Policy Bundle you can adopt per team. The library spans four value classes:
Lower $/token or tokens/loop. live now
Prevent loss or risk. live now
Shared, vetted context per group — raises output quality. roadmap
Catch conflicting repo context before tokens are spent. roadmap
The moat isn't any single technique — it's the governance framework that keeps absorbing new ones, as a managed ruleset that updates as the field advances.
Thank you. Where does this land for you — and what's missing?