Tvameva Platform
The platform enterprises build governed AI on.
Enterprises don't lack LLM APIs — they lack the governed assembly experience. One runtime that bakes in identity, tenancy, audit, metering, and non-bypassable human approval checkpoints, so every AI application is enterprise-safe by construction, not by hope.
The foundation
12 governed services. Every one of them always on.
Identity, execution, and observability — every service is consumed by the same typed interface. No application escapes the governance model.
Identity & Access
- Federated SSO / IdPWorkOS + bring-your-own
- Per-tenant isolationGCP project per customer
- BYOKCustomer-held Anthropic keys, KMS-encrypted
Runtime & Execution
- Agent orchestrationGoverned, scoped, auditable
- Recoverable workflowsSurvives refresh / restart
- Tool risk registryPer-app scope enforcement
- Non-bypassable checkpointsHigh-risk actions require human approval
Observability & Metering
- Metadata-only audit trailHMAC-signed, 5-year retention
- Outcome-based meteringAlso seat / flat-rate
- Cost-event pipelineDual-view FinOps discipline
- Guardrail dry-runCatch policy violations before they ship
- EncryptionCMEK at rest · TLS 1.3 + mTLS in transit
What you build with
Five capabilities. One governed interface.
The build surface packages the 12 governed services into a developer experience that makes enterprise-safe AI the path of least resistance — not an afterthought. And it opens that same surface to business technologists through App Studio.
Platform SDK
One typed client and CLI over all 12 governed services. You can't build an app that skips identity, audit, metering, governance, guardrails, or human checkpoints. Enterprise-safe by construction, not by hope.
App Manifest
A declarative, schema-validated contract that makes apps tenant-scoped content rather than code forks. Every application declares its permissions, tool access, and human checkpoint requirements up front.
Engineering Harness
init / validate / run --local / deploy commands with mechanical build gates: security lint, guardrail dry-run, quality thresholds, cost projection, and dual-view FinOps per build. Every build accountable before it ships.
Experience Layer
Generated cockpit UI from the App Manifest, reference-architecture templates, and a tenant app registry. The runtime becomes navigable without custom frontend investment for every application.
App Studio
App Studio
No-code surfaceThe visual builder that opens the platform SDK to business technologists — assemble a governed workflow app by describing it, no code. Same identity, audit, metering, guardrails, and checkpoints as every other app.
How it governs
The model decides what. The harness governs when, where, and how.
Strong models still fail real enterprise work without an environment that constrains and governs them. Anthropic's own experiments show the same model failing a task in 20 minutes without a harness, and succeeding over 6 hours with one — the environment changed, not the model. The Tvameva platform is that environment: the enterprise-grade, governed harness for agentic applications. It has five subsystems, each mapped to real platform capabilities.
App Manifest + declared agent scope
Every agent session begins with a published, version-controlled set of instructions. The App Manifest declares what an agent is permitted to do before it does anything.
Recoverable execution + signed audit trail
Long-running agentic work survives restarts. Every action appends to a signed, immutable audit record — so the current state is always reconstructable and the history is never lost.
Evaluation harness + guardrails + quality gates
Automated graders check agent outputs against quality rubrics on every run. Guardrails catch policy violations before they reach production. Human approval gates close the loop on anything high-risk.
Authority-boundary checks + tool risk registry
Agents cannot exceed the permissions declared in their App Manifest. The platform enforces scope boundaries before any tool is invoked — not by convention, but in code.
Orchestrator + recoverable workflows + human checkpoints
The orchestrator governs when agents start, pause, hand off, and complete. Human checkpoints are non-bypassable stops built into the lifecycle — not optional review layers added on top.
Human-Governed AI Pods are the delivery expression of this harness. AI agents do the work inside declared scope. Non-bypassable checkpoints enforce human authority on anything high-risk. The platform ensures neither the model nor the agent can exceed what was declared — not by policy, but by construction.
Production agent performance
Evaluation. The verification layer of governed delivery.
AI systems don't behave the same way twice — the same request can produce a different answer every run, which makes traditional pass/fail testing useless. The evaluation capability is an automated QA team for agentic applications: it runs real tasks against your system the way a customer would, watches everything that happens — the response, the documents produced, the tools and services called, the resulting data state — and grades each run against quality rubrics instead of brittle exact-match assertions. Every failure comes back with evidence, so an engineer knows the fix in minutes.
Answer quality / faithfulness
LLM-as-judge scoring against rubrics — not brittle exact-match assertions.
Document artifact checks
Right file, right format, required sections — verified on every run.
Tool-invocation checks
Correct internal tool called with correct arguments, pulled from execution trace.
External service / MCP checks
Validates that agents reached the right external services with the right payload.
API-contract checks
URL, method, and payload shape validated against declared contracts.
Database-state checks
Confirms the system landed in the correct post-run state — not just that it ran.
The OpenTelemetry pipeline continuously measures what agents actually do in production. The evaluation harness grades that behavior against quality rubrics. Drift from mandate is detected and corrected. The loop repeats. This is how Human-Governed AI Pods keep agents performing to their core mission — not through one-time review, but through continuous, instrumented, evidence-rich oversight.
Governance model
Human-Governed AI Pods.
Humans set the mission and own the outcomes. AI agents execute the work. The platform enforces the boundary between the two — through non-bypassable checkpoints, authority-boundary checks, and an immutable audit trail. No agent count framing. No agent personalities. Just governed execution.
Non-bypassable checkpoints
High-risk agent actions require explicit human approval before execution. The checkpoint cannot be skipped in code — it's enforced at the platform level, not by convention.
Customer-held keys (BYOK)
Customer supplies their own Anthropic API key. Tvameva encrypts it via KMS and never sees the plaintext value. Your models, your keys, your cloud.
Per-tenant GCP isolation
Each customer runs in their own GCP project. Blast radius is bounded by construction. No shared compute, no shared storage across tenants.
Metadata-only audit trail
Every agent action, human approval, and tool invocation is logged as metadata — HMAC-signed for integrity, retained for five years, queryable for compliance.
Authority-boundary checks
Agents cannot exceed the permissions declared in their App Manifest. The governance engine enforces scope boundaries before any tool is invoked.
Outcome-based metering
Usage tracked as business outcomes — not seat counts, not token volumes, not API calls. Pricing that aligns to the value delivered, not the compute burned.
How we build our own software
The Chief Architect, working with AI agents, delivered ~17× lower cost than a traditional engineering pod.
~200 hours and ~$6,000 of agentic compute — about $56K all-in — accomplished what a traditional 4-person engineering pod would take 120 days and ~$960,000 to build. These are Tvameva's own build economics, measured on our own platform builds by the same OpenTelemetry pipeline the platform gives every tenant.
Agentic all-in
Chief Architect 200 hrs @ $250 + ~$6K agentic compute
OTel-measured · PropelEdge R1.0
Human-pod equivalent
4-person pod × 120 days @ $250/hr
Comparable scope · Same loaded rate
Lower cost
$56K agentic vs $960K human-pod equivalent — same scope
Apples-to-apples · Chief Architect rate
Measured session cost
One 29-minute OTel-instrumented Chief Architect session
High confidence · Real Anthropic bill · 2026-06-11
Assumptions: 4-person pod = AI Engineer · Full-stack Engineer · DevOps/Test Engineer · Architect. 120 days × 8 hrs × $250/hr (same loaded rate as Chief Architect, apples-to-apples) = $960,000. Chief Architect: 200 hrs × $250/hr = $50,000 + ~$6,000 OTel-measured agentic compute = $56,000 all-in. These figures describe Tvameva's own build economics — not a projection of what a customer will save. Measured by the same OpenTelemetry pipeline the platform gives every tenant.
Proof the services work
Three solutions. One runtime.
PropelEdge, InsightLens, and EngageOS each consume the same 12 governed services. They don't share code — they share governance. Every application inherits identity, tenancy, audit, and checkpoints from the runtime, not from their own implementation.
PropelEdge
Revenue Orchestration
Proposal automation, RFP response, competitive intelligence — all governed by the same identity, audit, and metering services. The most mature application on the runtime.
InsightLens
Agentic Finance Intelligence
Executive scorecards, forecast acceleration, and audit-ready compliance reporting — agents governed by the same authority-boundary checks and human checkpoints as every other app.
EngageOS
Composable DXP
The composable digital experience platform — content orchestration, multi-channel publishing, and personalization backed by the same governed runtime every other solution uses.
Secure by construction
Security isn't a feature. It's the foundation.
On most platforms security is bolted on after the product works. Ours runs the other way. The platform enforces identity, isolation, encryption, and an unbroken audit trail at the foundation — so every app built on it inherits those controls automatically. Each customer runs in their own isolated cloud; data stays in their environment, and only signed, content-free metadata ever leaves it. Every agent operates inside a permission boundary it cannot exceed, and every action is recorded. Doing the secure thing isn't a discipline you maintain — it's the only path the system offers.
Federated SSO + MFA via customer IdP (WorkOS), with RBAC + ABAC and mandatory tenant scope
Per-customer GCP project isolation — blast radius bounded by construction, not convention
Customer-held Anthropic API key (BYOK); Tvameva never sees the plaintext value. CMEK at rest.
TLS 1.3 + mTLS in transit across all service boundaries
Metadata-only, HMAC-signed, append-only audit trail — 5-year retention, forbidden-field enforcement
Authority-boundary checks + guardrails: agents can't exceed declared permissions; high-risk actions require human approval
Prompt-injection-resistant agent design — ingested content is data, not instructions
Build-time security gates: lint, guardrail dry-run, and cost projection on every build
Continuous security review on every production-affecting change — hard security gate + QA gate
Minimal sub-processors: Google Cloud, Anthropic, WorkOS only
Designed to SOC 2 / ISO 27001 technical-control standards; certification on a phased roadmap
GDPR-aligned controls; Data Processing Agreement available
The same pattern we hold our own AI delivery team to — declared authority, human approval on anything destructive, complete audit trail — is what the platform enforces for every agent built on it. Compliance posture: designed to SOC 2 / ISO 27001 technical-control standards (certification on a phased roadmap); GDPR-aligned controls with a Data Processing Agreement available; minimal sub-processors (Google Cloud, Anthropic, WorkOS only).
Structural difference
Not a different kind of SI. A different kind of platform.
The difference isn't that we use more AI. It's that the governance model is baked into the runtime — not bolted on through process, not delegated to individual developers. The table below is qualitative, not a cost comparison.
| Dimension | Traditional SI | Tvameva Platform |
|---|---|---|
| Governance model | Governance through process — checklists, review meetings, manual approvals after the fact | Governance through code — non-bypassable authority-boundary checks enforced at the platform layer before execution |
| Tenancy | Shared environment, shared compute; logical separation via config | Per-customer GCP project isolation; blast radius bounded by construction, not by convention |
| FinOps depth | Monthly invoice; retroactive cost visibility | Cost projection per build before it ships; dual-view FinOps per build and per run |
| Audit trail | Audit logs when something breaks; retroactive reconstruction | HMAC-signed metadata audit on every agent action, every human approval, every tool call — from day one |
| Keys and data residency | Vendor holds credentials; data may traverse vendor infrastructure | BYOK — customer holds their own Anthropic keys, KMS-encrypted. Your data stays in your cloud. |
Get started
See the governed runtime in action.
We'll walk through the platform live — identity, checkpoints, audit trail, tenancy, and how one of the three solutions runs on top of it. Thirty minutes, no deck.