Which GPT model should I use?

Usually a mix: a capable model for hard steps and a cheaper or smaller one for easy, high-volume steps. We route by task and prove the choice with evals rather than paying for the biggest model everywhere.

How do you keep costs under control?

Prompt and context trimming, caching, model routing by difficulty, and hard token budgets, all visible in observability. We treat cost as a first-class metric alongside quality and latency.

★ OpenAI GPTLLM Models8-week engagement

OPENAI GPT · DIRECT INTEGRATION

Direct GPT integration. Structured output, validated, observable.

OpenAI's GPT models are a strong, well-tooled default with broad capabilities and a mature ecosystem. We integrate them directly, with the evals, cost control, and structure that production demands.

LLM APIEval pipelinesTypeScriptVector store

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Cycle

8 weeks · fixed price

Stack

OpenAI API, direct

Output

Production code + eval suite

Handoff

Full source ownership

[THE SHORT VERSION]

The default frontier model, engineered for production.

GPT models are capable, broadly supported, and backed by mature tooling for structured output, function calling, and embeddings. The gap between a demo and a product is the same as always: evals, retries, cost and latency control, structured output validation, and a vendor-neutral abstraction. That gap is the work we do.

When it fits

General-purpose reasoning, generation, and extraction
Function calling and structured output workflows
Teams wanting a broadly supported, well-documented model

When it does not

On-prem-only requirements (use an open-weight model)
Tasks where a cheaper model meets the eval bar

[HOW WE BUILD IT]

How we build with OpenAI GPT.

Direct API, thin abstraction

Calls go straight to the OpenAI API behind a small provider interface, so switching or adding models stays a config change.

Structured output, validated

We use structured output and function calling, then validate against a schema. No hoping the JSON parses.

Evals before you trust it

An eval set from your real tasks gates every prompt and model change. Quality is measured, not vibed.

Cost, latency, and fallback

Token budgets, caching, streaming, and a fallback path, with observability on every call.

[WHAT YOU GET]

What the engagement leaves behind.

Direct

No orchestration framework

Schema

Structured output validated

Eval-gated

Quality measured, not assumed

Observed

Every call, cost and latency

[METHODOLOGY · K-FRAMEWORK]

Integrated through the
K-Framework.

Every model we integrate runs through the same operating system. Three pillars, sixteen layers, one Compound Growth Loop. The methodology that keeps AI work from rotting after the first ship.

Read the K-Framework

Foundations

Direct API integration with the model. No LangChain, no orchestration vendor, no agent framework built on quicksand. Typed contracts, the same way we wire up Postgres.

Amplification

An eval suite built from your real tasks gates every prompt and model change. Quality is measured before it ships, not vibed in a demo.

Judgment

Governance, audit, and oversight wired in from day one. Who called what, with which prompt version, at what cost. Your auditors get answers, not screenshots.

[OBSERVABILITY]

Observability your team can read.

A model in production without observability is roulette. We instrument every integration so engineering and finance can see the same numbers, and so a regression at 3am surfaces before a customer opens a ticket.

Instrumented

Cost per call

Tokens in, tokens out, dollars spent. Sliced by feature, tenant, and route. Budgets enforced where it matters.

Instrumented

Latency p50 / p95 / p99

Real distributions, not averages. We know which routes are slow, and why.

Instrumented

Eval pass rates

The same eval suite that gates a release runs continuously in production. A regression on real traffic surfaces fast.

Instrumented

Prompt + completion logs

PII scrubbed at the proxy, shipped to your SIEM. Retention controls match your compliance window.

Dashboards your team owns, not ours. At handoff you get the queries, the alerts, and the runbook. We are not in the path to read your metrics.

[COMMON QUESTIONS]

Questions we get asked.

Which GPT model should I use?: Usually a mix: a capable model for hard steps and a cheaper or smaller one for easy, high-volume steps. We route by task and prove the choice with evals rather than paying for the biggest model everywhere.
How do you keep costs under control?: Prompt and context trimming, caching, model routing by difficulty, and hard token budgets, all visible in observability. We treat cost as a first-class metric alongside quality and latency.

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