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SHIPPING Q1 · 3 AI-NATIVE SaaS PRODUCTS300+ SALESFORCE PROJECTS DELIVERED15+ YEARS · TRUSTED IMPLEMENTATION PARTNERAI AGENTS · LLM · RAG · MLOPS · NOW HIRINGLIVE IN PRODUCTION ACROSS 3 INDUSTRIESSHIPPING Q1 · 3 AI-NATIVE SaaS PRODUCTS300+ SALESFORCE PROJECTS DELIVERED15+ YEARS · TRUSTED IMPLEMENTATION PARTNERAI AGENTS · LLM · RAG · MLOPS · NOW HIRINGLIVE IN PRODUCTION ACROSS 3 INDUSTRIES
AI & Data · LLM Development

LLM apps that ship and stay shipped.

Structured outputs, real evals, and cost discipline built in.

We build domain-specific LLM apps that hold up in production, not demoware that breaks in week two. Structured outputs you can rely on, evals that catch regressions, prompt caching that controls cost, and observability so you actually know what's happening at runtime.

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01What we deliver

Engineering, not slides.

01

Structured Outputs

Schema-validated JSON, tool-calling, constrained decoding. Your downstream code can rely on the shape.

02

Evals Pipeline

Regression tests for prompts. CI gates that fail on accuracy drops. No more 'it worked yesterday'.

03

Prompt Caching

Aggressive cache strategies on Anthropic / OpenAI / Bedrock. Often 60-80% cost reduction on read-heavy workflows.

04

Multi-Model Routing

Cheap model for easy tasks, big model for hard ones. Automatic fallback on rate limits or outages.

05

Document Processing

OCR + extraction + classification pipelines on PDFs, scans, photos. Built-in field-level confidence scoring.

06

Observability

Per-call tracing, token spend dashboards, latency p95s, output-quality scoring. You see what the model sees.

02How we work

From idea to production.

01

Use-case framing

We translate 'use AI for X' into testable evaluation criteria before writing a line of code.

02

Baseline + evals

Build the eval suite first. Then build the system. Then measure both together.

03

Iterative shipping

Ship weekly, measure against evals, refine prompts/models/retrieval. No big-bang launches.

04

Handoff

Documentation, runbooks, dashboards. Your team can operate it without us.

03Stack

Models & tools we reach for.

Anthropic Claude 4.7OpenAI GPTPydanticInstructorLangSmithBraintrustWeights & BiasesLogfire
04FAQ

Common questions.

For one-off tasks, do. For features inside your product, you need structure, evals, and cost control. That's where we come in.
First production deploy in 4-8 weeks for most use cases. Faster if requirements are sharp.
Yes, but we recommend it only when prompt engineering + retrieval can't hit your accuracy targets. Fine-tuning is the last lever, not the first.
05Next step

Let's scope it together.

Free 30-minute call. Bring your problem statement and current stack, and we'll tell you honestly whether it's worth the build.