AI you can put in production — and measure.
We build practical AI features grounded in your data: document intelligence, internal copilots, and automations with eval harnesses, guardrails, and observability.
Most AI demos look great on stage and break in production. Ours don't, because we treat AI like any other system: instrumented, evaluated, and operable.
We focus on AI work that earns its keep — replacing repetitive workflows, accelerating expert tasks, and pulling signal out of unstructured data. We won't bolt on a chatbot just because it's trendy.
Every AI feature we ship has an evaluation harness, a fallback path, and a cost-and-quality dashboard. You'll always know what it's doing and what it's costing you.
The work, in plain language
No buzzwords. Each item below is something we'll do for you, in the order we'll do it.
- 01
Find the AI-shaped problem
Most teams have one or two workflows where AI is a force multiplier — and many where it's a distraction. We help you tell them apart.
- 02
Ground in your data
We build retrieval pipelines (RAG) over your corpus with explicit citation, version pinning, and observability — not magic.
- 03
Build the eval harness first
Before we ship a single feature we define what 'good' looks like and instrument it. AI without eval is gambling with the business.
- 04
Wire in guardrails
PII redaction, prompt injection defense, content filtering, and human-in-the-loop checkpoints, all selectable per workflow.
- 05
Operate with cost and quality dashboards
Per-tenant cost, latency, quality, and refusal rates — exposed to your team and ours. No surprise bills.
- 06
Iterate with feedback loops
We close the loop on labeled feedback so the system gets better in production, not just in benchmarks.
What lands in your repo
- Use-case prioritization report
- RAG / agentic pipeline implementation
- Eval harness with regression suite
- Guardrail layer (PII, injection, content)
- Cost & quality dashboards
- Model and provider abstraction layer
- Human-in-the-loop UIs where needed
- Runbook for AI incidents and rollbacks
Who this fits
- Ops teams drowning in document workflows
- Support orgs with high repeat-question volume
- Sales/CS teams that need internal copilots
- Regulated industries needing AI guardrails
- Companies tired of demoware and POCs
- Founders shipping AI as a real product surface
A healthcare SaaS cut intake time 62% with a HIPAA-aware AI document pipeline
We replaced a 4-step manual intake with an LLM extraction pipeline. PHI is redacted at the edge, citations are linked, and the eval harness catches drift weekly. The team eliminated $210k of annual labor.
- Faster intake
- 62%
- Annual labor saved
- $210k
- PHI incidents
- 0
Things people ask before signing
If your question isn't here, send it our way and we'll answer plainly.
We're model-agnostic. We typically deploy through OpenAI, Anthropic, or AWS Bedrock — whichever fits the workload, the data residency, and the budget. We always isolate behind a provider abstraction.
Ready when you are
Let's build something durable.
Tell us about your goals. We'll respond within one business day with next steps.