Service
Classification and extraction you can ship to production
Structured output from unstructured input — route tickets, extract entities, summarize threads — with schema validation, fallbacks, and observability built in.
Who this is for
Product teams replacing manual triage, copy-paste summarization, or brittle regex pipelines with model-assisted classification and extraction — where downstream systems need typed, validated output, not free-form text.
Problems we solve
Common failure modes when copilot, retrieval, or middleware features are bolted on without an integration plan.
- Prompts that return prose your APIs cannot parse — no schema enforcement, retries, or fallback when JSON shape is wrong
- One-off scripts or notebooks that bypass auth, logging, and the same deployment path as the rest of the product
- Retrieval added to problems that are really structured output — adding latency and ops burden without retrieval benefit
Typical deliverables
- Output schemas and validation layer — Zod, JSON Schema, or your existing types — with reject-and-retry or safe fallback when the model misses the shape
- Server-side inference path through LLM middleware — smaller models for classification where appropriate, temperature and token limits tuned per workflow
- Batch and streaming handlers for ticket queues, document uploads, or in-app actions — with idempotency and audit logs per tenant
- Eval datasets from real workflow samples — routing labels, extraction fields, summary structure — with CI gates before prompt or schema changes ship
How we deliver
Your eng team stays on the roadmap. We handle the AI integration layer — scoped sprints, PRs to your repo, and handoff docs so your team can operate what we ship.
We confirm the task is structured output, not open-ended Q&A or live lookup, before designing the pipeline. The audit maps input sources, target schemas, error rates your downstream systems tolerate, and where human review fits. A prototype runs against representative inputs in staging; production rollout stays behind feature flags with shadow-mode comparison to your current rules or manual process.
Step 1
Technical audit
Map your architecture, API boundaries, data flows, and auth model. Identify the lowest-risk, highest-value integration point.
Step 2
Architecture & prototype
API contracts, middleware design, and a working proof against your real stack — validated before full build commitment.
Step 3
Build & deploy
Production code in your repo. Staging, load testing, and canary rollout behind feature flags — with runbooks for your team.
Step 4
Operate & expand
Monitor latency, cost, and output quality. Iterate on evals and prompts, then extend to the next workflow boundary.
Related guides
Deeper technical notes from our resources library.
When not to use RAG
RAG is the default answer for every AI feature — but often the wrong one. A decision guide for engineering leaders scoping retrieval, tools, and middleware.
June 6, 2026
Eval pipelines for LLM features — what they are and how to build one
A practical guide to golden sets, property-based scoring, and CI gates — so prompt and retrieval changes do not silently break production copilots.
June 10, 2026
What production-ready LLM integration actually means
A practical checklist for engineering leaders — beyond the demo and before you call an AI feature shipped.
May 15, 2026
Common questions
- When is classification or extraction better than RAG?
- When you need to map input to a fixed schema — route a ticket, extract fields from a document, label urgency — not synthesize an answer from a document corpus. If retrieval is not required, a schema-governed LLM call through middleware is usually simpler and cheaper. We assess that in the architecture phase.
- How do you ensure outputs match our schema?
- Structured generation with validation on every response — reject malformed output, retry with constrained prompts or a fallback model, and log failures for review. Downstream systems never receive unvalidated free-form text when a typed field was expected.
- Can this run in batch on existing queues or only in real time?
- Both. We integrate with your existing job runners, webhooks, or in-app triggers — same auth and tenant scoping as interactive features. Batch pipelines get the same observability and eval discipline as user-facing calls.
Scope an integration for your stack
Describe the feature you are planning — we will map architecture, effort, rollout strategy, and what production-ready means for your system.
Get an integration plan