SERV Reasoning

Production-grade reasoning,

one line of code.

The reasoning engine that sits between your application and any frontier model. Same SDK, same prompts — at a fraction of frontier model cost, with reliability that survives procurement.

Get API Access

Try the playground

Frontier models break in production.

Every enterprise hits the same three walls.

Reliability

of enterprises achieve measurable AI ROI on the majority of their projects.

IDC, 2026

Production agents fail differently than chatbots. Hallucinations compound. Retries spiral. Demos don't survive deployment.

Cost

$0K

Per agent per month at full frontier pricing.

Internal benchmarks

One human request becomes hundreds of model calls. At agentic scale, the math doesn't work

Auditability

10/N

regulated procurement gates that frontier APIs clear.

Healthcare, finance, gov, defense

They need every reasoning step traceable. Frontier APIs don't give you that.

You cannot prompt your way out of an inference layer that was not designed for the workload.

Three mechanisms.

One reasoning architecture.

These mechanisms are the productized form of BRAID — published at arXiv:2512.15959. Currently in peer review.

Bounded reasoning graphs

Tasks decompose into structured steps with explicit dependencies. Each step has a defined input schema, output schema, and validation contract. The model can't wander because the graph won't let it.

Schema-forced execution

Outputs conform to specifications, not arbitrary prose. Parse failures disappear. Latency drops. Reasoning tokens stop multiplying without constraint. Most of the cost was hiding here.

Intelligent model routing

Easy work goes to cheaper models. Specialized work goes to specialists. Frontier models only fire where they actually matter. The frontier isn't the problem. Calling the frontier for every step is the problem.

Model Catalogue

Multiple SERV-Reasoning classes, one API. Pick the tier each workload deserves, swap to SERV-enabled frontier models freely.

Verification and Prompt Protection Layer

Every output validated before it leaves - the audit-grade trail regulated industries require.

Graph Creation vs Execution

Planning relies on frontier models, repeatable execution leans on smaller models.

Schema-Forced Execution

Outputs conform to specifications, not arbitrary prose. Parse failures vanish; latency and cost drop.

Bounded Reasoning

Tasks decompose into structured steps with explicit dependencies.

Read the research paper

Where the savings actually live.

Five workload shapes that account for the curve.

Agent loops with tool calls

10K+ queries/day. Long chains amplify every cent of inference cost

~30-100× cost reduction

Classification, extraction, routing

High volume, narrow output. Frontier models are massive overkill.

~74× cheaper at parity

Repeated workflows

Invoice processing, support triage, document QA — same shape, different inputs.

~80-90% cost cut observed

Structured generation

JSON outputs, function calling, schema-bound responses. Where parse failures live.

~0% parse failure rate

Plan-then-execute pipelines

Strong model plans, cheap model executes. The economic split that makes the math work.

best-in-class on multichallenge benchmarks

Same SDK. Same prompts.

Different brain.

OpenAI

The entire installed base of LLM-powered software is addressable without rebuild cost.

OpenAI SDK-compatible

Anthropic SDK-compatible

2-minute integration

No vendor lock-in — swap any frontier model, keep the reasoning layer

Get API Access

Read Docs

Six models.

Pick the tier the workload deserves.