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Applied AI · Production, not demos

AI features that survive the demo.

Anyone ships a chatbot in a weekend. We build LLM features with evals, guardrails, and cost controls — so the thing still works six months from now, in front of a board, under real traffic.

Discuss an AI use case Currently integrating AI into 4 live products
18
LLM features in production
<2s
P95 latency target
100%
Shipped with eval suites
Philosophy

Where AI earns its keep — and where it doesn't.

We treat AI as one tool in the stack, not the product. The difference between a feature that ships and a demo that dies is knowing when to reach for it.

Green light

We build with AI when

  • 01

    It removes repetitive human work at scale.

    Classification, extraction, routing, and summarization at volumes where a human reading every row no longer makes business sense.

  • 02

    It turns messy inputs into structured signals.

    Unstructured documents, conversations, or user inputs that need to become scored, ranked, or indexed data your product can act on.

  • 03

    It measurably reduces time-to-outcome.

    Drafting, search, and assistance features where the cost of latency and tokens is lower than the human-hours it replaces.

Red light

We push back when

  • 01

    The product works fine without it.

    If AI is there to be mentioned in the pitch deck, we'll tell you. Every model you ship becomes your problem to evaluate, monitor, and pay for.

  • 02

    The data isn't ready.

    Bad grounding beats no grounding in exactly zero cases. We stop and fix the data layer before wiring up retrieval or fine-tuning.

  • 03

    Fundamentals are broken.

    If onboarding leaks users or the core flow confuses people, a copilot will not save you. We fix the product, then consider the AI.

Integration patterns

What we actually ship.

Four patterns that consistently return their investment. Everything else, we evaluate case by case — and say no when the numbers don't work.

01 Pattern

Workflow automation

Take high-volume, rule-shaped tasks off your team's plate. Classify tickets, route cases, summarize transcripts, extract fields from documents — with evals so you catch drift before users do.

  • Structured output
  • Eval harness
  • Cost telemetry
In practice

Background jobs triggered by queues or webhooks. Structured output with schemas. Cost-tracked per run.

02 Pattern

Retrieval & grounded answers

Make the knowledge already in your product accessible. Semantic search over your docs, tickets, and data — with citations, not hallucinations, and filters that respect permissions.

  • Vector + BM25
  • Permission-aware retrieval
  • Cited responses
In practice

Hybrid search (vector + keyword), reranking, source-grounded responses, and query caching for latency.

03 Pattern

Decision support

Turn messy inputs — free-text notes, transcripts, PDFs — into scored, ranked signals your team can act on. Humans stay in the loop; the model handles the tedium.

  • Confidence scoring
  • Human-in-the-loop
  • Audit trails
In practice

Scoring pipelines, triage queues, structured summaries with confidence levels and human override.

04 Pattern

In-product assistance

Contextual drafting, form completion, and guidance — placed where users already are. Not a floating chatbot that nobody uses. Shipped behind a flag, measured against a control.

  • Feature flags
  • A/B experiments
  • Graceful fallback
In practice

Inline suggestions, smart defaults, step-by-step helpers — with A/B tests and opt-out controls.

How we work

Discipline first, models second.

Reliable AI is less about the model and more about the harness around it: the context you feed, the evals that catch regressions, the cost ceilings you hold, and the fallback when the model refuses.

  1. 01

    Start with the product, not the model.

    We name the user outcome and the success metric before we touch a model picker. If the metric can't be defined, the feature isn't ready.

    Deliverable Problem brief · success metric · scope boundaries

  2. 02

    Ground against trusted data.

    We design what the system can and cannot access — with permissions respected end-to-end. Retrieval is scoped, cited, and audited.

    Deliverable Retrieval scope · permission model · citation schema

  3. 03

    Guardrails by design, not as patches.

    Constraints, fallbacks, and failure modes are modeled upfront. The feature behaves predictably when the model is confused, rate-limited, or wrong.

    Deliverable Guardrail spec · fallback flow · refusal handling

  4. 04

    Evaluation before launch, not after.

    A fixed eval suite tuned against your real data gates every deploy. We measure regressions like we measure test failures — because they are.

    Deliverable Eval harness · regression gate · quality dashboard

  5. 05

    Cost and latency as first-class metrics.

    Every call is budgeted and tracked. Model choice, caching, and routing are tuned to keep unit economics positive at your scale.

    Deliverable Cost telemetry · latency SLO · routing strategy

Let's talk

Exploring an AI feature?

Send us the use case and what you're trying to measurably move. If AI isn't the right tool, we'll tell you — and save you the integration cost.

Discuss an AI use case See how we work
Currently integrating AI into 4 live products · responses within 24h No generic chatbots, no demos that die after launch.