Prodis Labs, Inc. · Memo № 002 · Rev 2026.03.05 · Doc 002-A · Page 01 / 01
From Prodis Engineering
To Engineering teams shipping AI over production APIs
Re Backend control between the model and the user
Keywords backend-owned runtime · typed endpoint contracts · evidence contracts · deterministic derivation · auditability · no API rewrites

Model proposes.
Backend executes.

Prodis is the backend-owned runtime for AI over production APIs. The model interprets. Code compiles, validates, executes, derives, checks evidence, and refuses when required. Synthesis happens only after answer readiness. Works with OpenAI SDK, Anthropic SDK, LangChain, MCP, direct tool calls, and in-house loops. No API rewrites.

§ 01

Premise.

The demo passes. Production is where it breaks.

Every production AI eventually fails the same way. A confidently wrong answer reaches a user¹. An action runs that should have been blocked². A decision is made that the team cannot reconstruct after the fact³. The assistant refuses gracefully, or it does not⁴. Polished language is not correctness, and the model is not the authority for that distinction.

§ 02

Stack.

Today's agent stacks run the loop. They do not own correctness.

OpenAI SDK, Anthropic SDK, LangChain, LangGraph, MCP, direct tool calls, in-house loops — useful tools, incomplete control boundary. They call models, route tools, stream responses, and wire workflows. They do not make the backend the authority for answer eligibility over business APIs. In the usual loop, the model still decides whether the evidence is enough. Prodis moves that boundary into code.

Fig. 01 — Stack vs. Runtime Drawn 2026.03.05
Today's Agent Stack
  1. 01user asks
  2. 02model picks tools
  3. 03api returns data
  4. 04model decides if enough
  5. 05model writes answer
answer
Prodis Runtime
  1. 01model interprets intent
  2. 02plan compiled + validated
  3. 03authorized APIs execute
  4. 04result derived in code
  5. 05answer readiness checked
synthesis only when ready
Prodis owns the control plane, not just the final check. Intent, plan, permissions, evidence, derivation, readiness, synthesis: each step has a backend-owned contract.
§ 03

Method.

From intent to answer readiness, code owns the control plane.

Prodis is a runtime, not a wrapper. The model still proposes: it interprets language and suggests structured intent. The backend compiles that intent into typed endpoint contracts, validates executable plans, runs only what is permitted, records evidence, performs deterministic derivation, checks answer readiness, and refuses when the request cannot be safely answered. Synthesis is presentation, not fact discovery.

§ 04

Field.

Three industries. Internal workflows and customer-facing commerce. The same verification problem.

Production AI often starts inside the team. Operators are the first audience that needs verified answers — answers they will act on, decisions that affect people downstream. The same control problem appears when the assistant reaches customers. Prodis pilots include:

01 FMCG retail chain Ops + commerce
02 Market research provider Internal ops
03 Fintech startup Internal ops
§ 05

Apparatus.

What runs inside the backend-owned runtime.

The control surface missing from the existing stack. Each capability is owned by code, not by prompts; each leaves an evidence trail the team can audit after the fact.

  1. 01

    Framework-native API introspection

    Understand live APIs through framework adapters instead of brittle manual prompt inventories.

  2. 02

    Typed endpoint contracts

    Capture request and response semantics in machine-usable contracts the planner can trust.

  3. 03

    Validated planning

    Reject invalid or unsupported plans before execution instead of repairing bad tool calls after the fact.

  4. 04

    Evidence sufficiency

    Make answer readiness explicit so the system knows when evidence is complete and when it is not.

  5. 05

    Deterministic derivation

    Use controlled computation for rankings, grouping, and synthesis over API data instead of prompt-side guesswork.

  6. 06

    Safe user and operator answers

    Support reliable answers across external and operator contexts without treating polished language as correctness.

  7. 07

    Hosted control plane

    Manage contracts, runs, evidence, and policy in one hosted service while your APIs remain the source of truth.

  8. 08

    Step-by-step auditability

    Inspect each step so teams can spot mistakes, review derivation, and debug with confidence.

§ 06

Integration.

30 min

A working assistant in 30 minutes.
Correctness controls from day one.

Works with your APIs out of the box, no rewrites. Install the Prodis adapter or plugin, keep your existing model-calling code, and ship a usable assistant without rebuilding your service around an AI framework.

Compatible with
OpenAI SDKAnthropic SDKLangChainLangGraphMCPdirect tool callsin-house loops
§ 07

RSVP.

Early preview by request.

Engineering teams shipping AI to production, with backend ownership of correctness. Join the private waitlist for early product updates and launch access.

RSVP → waitlist@prodis.ai