Service · AI Engineering

Production-ready AI features. Not demos.

40% of agentic AI projects will be cancelled by 2027. Gartner's number, not ours. We build the other 60%: RAG that survives a real corpus, evals that catch regressions, agent loops that stay in budget.

Book a discovery call → Read the SaaS case →

On this page: Who it's for What's included How we work Pricing FAQ

Who it's for

Teams shipping AI into a product, not pitching it.

AI/ML SaaS companies adding agentic surfaces to existing products.
Enterprise platform teams retrofitting LLM features into legacy software.
Healthcare and regulated buyers needing PHI-aware RAG and ambient AI.
Engineering leaders who've shipped a demo and now need it to survive Monday morning.

What's included

Six capability blocks across the AI stack.

RAG

Retrieval pipelines

Vector + lexical + reranker, evaluated against your gold set before launch.

Capability →

EVAL

Eval harnesses

Real test sets, regression tracking, gating on PR. The thing that keeps quality from drifting silently.

Capability →

AGT

Agent infrastructure

Tool use, planner-executor loops, budget ceilings, retry policies. Built for cost predictability.

Capability →

MCP

MCP integrations

Connect models to your data and tools via Model Context Protocol. Specialty stack from healthcare to finance.

Capability →

Fine-tuning + adapters

When prompting hits a wall, we tune. LoRA, QLoRA, full fine-tunes when the math justifies it.

Capability →

EHR

Healthcare-aware AI

Ambient clinical intelligence, EHR-integrated, BAA-signing. Cross-link to the healthcare vertical landing.

Healthcare vertical →

How we work

Spec the eval first. Then build.

AI-readiness brief

30-min call. We diagnose whether the problem is a RAG problem, an eval problem, or an agent-budget problem. Most teams are wrong about which one they have.

Eval-first build

We write the eval harness before the feature. If we can't measure better, we won't ship the change.

Production hardening

Cost ceilings, latency budgets, fallback paths, observability. The boring infrastructure that decides whether the demo survives the launch.

Pricing

AI engagements price as standard pods, plus a premium tier for AMC-shaped builds.

Most AI work runs as a Tier 1 staff-aug engagement with the AI-engineering capability dialed up. Healthcare AI work runs as a Tier 2 fixed-cost build.

AI-augmented pod

$50k–100k/mo

T&M + retainer · 3-8 engineers · 6+ month engagement

RAG, evals, agent infra
Production hardening
Tier 1 default

AI build sprint

$75k–150k

Fixed-cost · 8-12 weeks · single feature delivery

AI-readiness brief included
Eval harness shipped
Pre-RFP option

Healthcare AI build Tier 2

$300k–500k

Fixed cost · 6-9 month delivery · BAA-signing

Ambient clinical intelligence
EHR-integrated (eCW, ModMed, Greenway)
Healthcare vertical landing →

Featured engagement

Enterprise AI/SaaS platform — AI surface delivered with the React pod.

Tier 1 · SaaS · AI/ML

Enterprise AI/SaaS platform — AI surface delivered with the React pod.

Enterprise AI/SaaS platform · California, USA · agentic features inside the platform

Read the SaaS case →

FAQ — ai engineering

The AI questions buyers actually ask.

Do you build agentic features end-to-end?

Yes. Planner-executor loops, tool use, budget ceilings, retry policies. The thing most teams skip is the budget ceiling. We don't.

How do you measure 'good' for an LLM feature?

Eval harnesses. We write them before the feature, score every PR, gate releases on regressions. If we can't measure better, we won't ship the change.

Can you fine-tune?

Yes — LoRA, QLoRA, and full fine-tunes when the math justifies it. Most teams should not fine-tune. We tell you which side of that line you're on in the brief.

Do you build for healthcare AI?

Yes — see the healthcare vertical landing for ambient clinical intelligence, EHR integrations, and BAA-signing engagements.

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