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AI/ML EngineeringAI with a scoreboard, a fallback and a human in the loop.

AI earns its place on three conditions. The task is repetitive, expensive and judgment-light. There are enough historical examples to evaluate against, and the cost of being wrong is tolerable. Where those hold, the returns are large and fast. Where they do not, a rules engine is cheaper, more accurate and easier to defend. That is what we will recommend, even though it is a smaller invoice.

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Commitments

Every generated answer traceable to a source
Cited
In the loop until accuracy earns autonomy
Human

AI/ML Engineering

We tailor an AI model to your industry and your own information, then put it behind whatever the work needs: a chat window, a phone line, or a process that runs on its own. Before we train anything, we check whether a simpler approach would do the job.

You decide which steps run unattended and which wait for a person to approve them. Every action is logged, so you can see what was done and when.

Fine-tuning
Training an existing model on your own examples so it learns your terms, formats and tasks, which for one job is often more accurate and cheaper to run than a general model
Chatbots
Answering questions from your documents, policies and data, on your site, in your internal tools or in Teams and Slack, handing the conversation to a person when it cannot answer
Voice agents
Answering and making calls in natural speech, booking appointments, taking messages and routing callers, with anything that needs a person going to your staff
Agentic workflows
Carrying out multistep tasks across your tools, such as processing incoming documents, updating records or drafting replies

When would I want this?

  • You want every inbound call answered, so you never miss a lead
  • You want to search your SOPs and documents with AI
  • You want a custom AI that runs inside a highly regulated environment
  • You want a model that's an expert on your business and your industry
  • You want to detect unusual activity, such as fraud

Sounds like

You might recognize one of these.

  • Four people spend their week retyping data from PDFs.

  • We tried a chatbot and it confidently made things up.

  • Our forecast is a spreadsheet someone maintains by hand.

  • The board wants an AI strategy and we need it to be real.

What you get

What lands on your side, and stays there.

  • A labeled evaluation set owned by your domain experts
  • Measured accuracy, latency and cost per task against a baseline
  • Human-in-the-loop review interface with correction capture
  • Model- and vendor-agnostic abstraction with a defined fallback
  • Prompt-injection and data-leakage threat review
  • Monitoring for drift and cost, alerting to an owner

Shapes

How this usually runs.

  1. AI feasibility sprint

    2–3 weeks

    Fixed fee. We take one candidate use case, build an evaluation set and prove or disprove it on your real data. It ends in a straight answer on whether the use case is worth building, with the evidence attached.

  2. Production pilot

    6–12 weeks

    One workflow, one team, in production behind a review queue, with the accuracy and savings measured rather than asserted.

  3. Scale-out

    Ongoing

    Additional use cases on shared evaluation, observability and governance infrastructure.

What this includes

The work, specifically.

Not every engagement needs all of it. This is the range we cover and what each part is actually for.

  • Document intelligence

    Extraction from invoices, bills of lading, claims, EOBs, purchase orders and scanned contracts, with confidence thresholds that route uncertain cases to a person instead of guessing.

  • Retrieval-augmented assistants

    Question answering grounded strictly in your own documents, with citations, refusal behavior when the source is silent, and access control enforced at retrieval time rather than in the prompt.

  • Classification, routing and prioritization

    Triage of tickets, claims, leads and exceptions, deployed behind a queue so the model’s suggestion is reviewed until it has earned autonomy.

  • Forecasting and optimization

    Demand, capacity, maintenance windows and route or schedule optimization, evaluated honestly against the naive baseline, because a surprising number of models lose to it.

  • Evaluation and guardrails

    Every model ships with a labeled evaluation set, tracked accuracy and cost per task, prompt-injection defenses, output validation and a documented fallback for the day the provider has an outage.

Tooling

What we build it with.

No tool here was picked because it was new. Where we do reach for something novel, it is in one place, for a stated reason, and it is written down.

Models
  • Claude
  • GPT
  • Gemini
  • Llama
  • Open-weight, self-hosted
Retrieval
  • pgvector
  • Elasticsearch
  • Hybrid BM25 + dense
  • Rerankers
Classical ML
  • scikit-learn
  • XGBoost
  • PyTorch
  • Prophet
Operations
  • Evaluation harnesses
  • LangFuse
  • Cost telemetry
  • Drift monitors

Questions

AI & ML, honestly.

  • Not unless you decide it should be. We deploy on enterprise terms with training explicitly disabled, or self-host open-weight models inside your own boundary when policy or classification requires it.

  • Constrain the task, ground answers in retrieved sources, validate outputs against a schema, require citations, and let the system say it doesn’t know. Then measure how often it’s wrong on a fixed evaluation set, because unmeasured accuracy is just a feeling.

  • We instrument cost per task from day one and design for it: smaller models where they suffice, caching, batching. Unit economics is a design constraint in these projects, not a surprise at the end of the month.

Further reading

What we think about this, at length.

All posts

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Next step

Tell us what’s breaking.

Forty-five minutes, no charge, no deck. We’ll tell you what we’d do, what it would likely cost, and whether what you already have can be made to work.

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A person replies, not a sequence: within one business day, from someone who would be on the engagement.