One endpoint in front of every model — Claude, OpenAI, Gemini, and open-weight models — with routing, caching, cost control, observability, and governance applied before anything reaches a vendor.
Cut your AI spend as usage grows — and prove every call to your examiner. Built first for US credit unions and mid-size banks.
Agent traffic enters on the left. Tessra gates, routes, and prices it. What leaves is governed, attributed, and ledgered.
Reasons over the full task, scoring what a call should cost and where it should run. Surfaces budget drift, misrouted work, and quality regression as they emerge.
The foundation. Captures and prices every call into an integrated, task-anchored history — complete, validated, and attributable to the workflow that spent it.
Request-side gates govern what the call attempts. Ledger-side gates price the result and write the record.
TESSRA is an acronym — each letter names a plane of the control plane. Together they cover everything a regulated institution needs before any prompt reaches a model vendor.
Full observability across every model call — traces, latency distributions, output quality scores, and cost rolled up by org, team, and user.
Policy-as-code governance: model allowlists, data-residency rules, prompt-pattern blocks, and human-in-the-loop approval gates — version-controlled like your code.
PII redaction inline, before data ever leaves your perimeter. Names, account numbers, SSNs, and configurable custom patterns are masked or tokenised in every request.
Cache-aware routing and cheaper-model defaults keep costs down as usage grows. Spend attribution by org, team, and user gives finance the visibility they need.
One endpoint, cross-provider failover, warm-cache stickiness. Tessra routes on the execution profile (model and harness together), picking the right one per request by policy, cost, and price per completed task.
Tamper-evident, hash-chained record of every call. Designed to map to GLBA and BSA requirements and support examiner review without data preparation.
Every capability is designed around the real compliance demands of financial institutions — so your AI program is defensible, not just deployable.
Sensitive data — PII, account numbers, SSNs — is masked before it reaches any model vendor. Data never leaves your perimeter in plain form.
Allow only approved models and endpoints. Block unapproved or foreign-hosted models on sensitive data. Policy rules are auditable and version-controlled.
Hash-chained logs designed to map to GLBA and BSA requirements, and to support examiner review without custom data preparation.
SaaS, private VPC, or fully on-premises / air-gapped inside your boundary. All three modes share the same compliance feature set.
Cache-aware routing and cheaper-model defaults reduce spend as usage scales. Attribution by team and user gives finance visibility without extra tooling.
Claude, OpenAI, Gemini, and open-weight or self-hosted models — all accessible through one OpenAI-compatible API. No SDK changes required.
SSO and role-based access control so the right people see the right data — and auditors can verify who authorized what.
Tessra is built first for US financial institutions and designed to extend to any industry where model governance and auditability are non-negotiable.
A natural companion to Adalma's Cernio (financial crime) and Cura (compliance management). Tessra gives your AI program the control plane that NCUA and FDIC examiners expect to find.
The same engine with a HIPAA compliance pack — PHI redaction, access controls, and audit trails designed for covered entities and business associates deploying AI at scale.
Export-controlled data demands the strictest perimeter controls. Tessra's on-premises and air-gapped modes support environments where data cannot leave the facility.
Cache-aware routing, cheaper-model defaults, and budget attribution let you grow AI usage without a proportional cost increase. Tessra applies these automatically on every request — no manual tuning required.
External reference: Coinbase has publicly reported significant AI-spend cuts using this same routing-and-caching approach — their result, not a Tessra-produced number.
The industry just proved it. The same model run through a leaner harness can cost far less at equal quality, because the harness controls context, retrieval, tools, and the test-and-repair loop. Price per token is the wrong number. Tessra routes on price per completed task, and records every choice in the audit ledger.
The real unit of an agent run is model, harness, context strategy, tools, and reasoning tier together. Tessra makes that a first-class, declarable profile in your compliance pack, and routes to it per request.
Total spend across a task, including retries and failed attempts, divided by the work it actually finished. A cheaper token can be a more expensive result. Tessra reports the number finance actually cares about.
Every execution-profile decision is hash-chained, so an examiner can see which harness served a request, why it was chosen, and what it cost per finished task. No other gateway logs the profile.
In a public benchmark on a multi-million-line codebase, the same model run through a leaner harness finished tasks at more than two times lower cost, at comparable quality. And a model that is cheaper per token can cost more per completed task, because it burns more tokens to get there. That delta is execution architecture, not model intelligence.
External reference: Databricks coding-agent benchmark, 2026. Their published result, not a Tessra-produced number.
Pick the deployment that matches your institution's data-residency and risk posture. All three modes share the same compliance capabilities.
Single-tenant, US-hosted. Tessra manages infrastructure, updates, and uptime SLA. Suitable for institutions comfortable with third-party SaaS.
Deployed inside your AWS, Azure, or GCP environment. Data never leaves your cloud account. You control the perimeter.
Fully on-premises or air-gapped. Supports self-hosted open-weight models so nothing leaves your facility — suited for defense and highest-sensitivity environments.
Talk to us about putting Tessra in front of your AI stack — and giving your examiners the audit trail they need from day one.
Tell us a little about your team and we'll set up a walkthrough.