// service · ai & automation
AI & automation
LLM features, retrieval, agents, internal tools that delete manual work.
We’ve shipped AI features that move real numbers — multilingual PDF parsers built on GPT-4V + Claude that automate hours of manual review per day, RAG pipelines for internal knowledge bases that actually get used, and structured-output systems that produce JSON your engineers can trust. We treat LLM features like any other production system: cost-bounded, observable, evaluable, and recoverable when the model regresses.
Stack & defaults
Models
OpenAI / Anthropic / Bedrock
Orchestration
Vercel AI SDK / LangChain
Retrieval
pgvector / Turbopuffer
Structured output
JSON Schema + Zod
Workflows
Inngest / Trigger.dev
Inference
Vercel / Cloudflare AI
LLM observability
Langfuse / Helicone
Testing
Evals + Promptfoo
What you receive
Use-case validation
Before code: a written assessment of whether AI is the right answer here. Often it's not — we'll say so.
Production-ready feature
Streaming, retries, fallbacks, cost ceilings, abuse protection. Not a prompt in a chat box.
Retrieval pipeline
Chunking strategy, embedding pipeline, vector store, eval harness. Documented in plain English.
Structured outputs
JSON Schema-validated responses with self-healing on invalid output. No hallucinated fields downstream.
LLM observability
Per-call cost, latency, token use, eval scores. Dashboards your CFO can read.
Eval harness
Golden-set tests so you know when a model upgrade actually helps.
Timeline
Wk 1–2
Validation
Use-case fit, eval design, success criteria.
Wk 3–6
Build
Pipeline, retrieval, prompts, evals.
Wk 7–9
Hardening
Abuse, cost ceilings, fallbacks, observability.
Wk 10
Launch
Cutover, dashboards, runbook handoff.
FAQ
Should we even use AI for this?
Maybe. We open every engagement with a use-case assessment. If a regex or a database query gets you 90% of the way there, we'll tell you.
OpenAI, Anthropic, or open-source?
All three, depending on the workload. We architect to be model-agnostic so you can swap when costs/quality shift. We've shipped systems using GPT-4V and Claude on the same pipeline.
How do you handle hallucinations?
Structured outputs (JSON Schema validation), grounded retrieval, eval harness, and never letting a raw LLM response into a downstream system. Hallucinations don't disappear, but they become observable.
What about agents?
Sparingly. Most 'agent' problems are better solved as a directed workflow with a tight LLM step. We'll build true agentic loops only when the use case demands it.