Skip to content
01Services / AI Integration

AI that actually
earns its place in production.

Most AI demos die in the gap between 'works on the prototype' and 'survives a Tuesday afternoon.' We build systems that close that gap, with the evals, fallbacks, and observability the demos skip.

02The problem

Your AI proof-of-concept worked.
Then what?

The pilot was great. Leadership saw the demo and got excited. Then engineering started asking the awkward questions: how do we know it's still working next month? What happens when the model regresses? Who owns the prompts?

Production AI is mostly the unglamorous parts: eval harnesses, fallbacks, observability, cost monitoring, prompt versioning. We do those well, so the rest stays sharp.

  • POC demo got applause; production rollout keeps stalling
  • No way to know if the model just got worse
  • Costs trending the wrong direction and nobody can explain why
  • Prompts living in someone's local notebook
03Our approach

Boring infrastructure,
reliable AI.

  1. i.

    Map the workflow

    Where does AI add value, and where would it just add latency? We separate the genuine wins from the resume-padding.

  2. ii.

    Build the rails

    Eval datasets, retrieval indices, prompt registry, structured outputs, failure handlers. The infra you'd have built six months from now, built first.

  3. iii.

    Ship behind a flag

    Real users, low-risk path, eval scoring on every response. Confidence comes from data, not from vibes.

  4. iv.

    Observability + handoff

    Dashboards your team actually checks. Runbooks for when things drift. We leave when you can answer 'is the AI working?' without paging us.

04What you get

AI features that
survive Mondays.

You ship AI features your team can debug and improve without us. Costs are visible, regressions are caught, and the product roadmap stops being held hostage by 'wait, is the model still working?'

Most importantly: you can answer the board's questions about reliability with numbers, not narrative.

i
<2 wk
from kickoff to first eval suite
ii
100%
of production calls logged + evaluable
iii
30–60%
typical cost reduction vs. naive prompting
iv
0
prompts living in notebooks

What we typically reach for

Models

OpenAIAnthropicOpen-source

Patterns

RAGAgentsStructured outputsTool use

Infra

Vercel AI SDKLangChainPineconepgvector

Observability

EvalsTracingCost dashboardsDrift alerts
05Got an AI feature stuck in pilot?

Let's talk about what it would take
to put it in front of users.

30-minute discovery call. We'll be honest about whether your problem is an AI problem or something else.