Honeycutt Software

AI engineering consultancy

AI that works in production, not just the demo.

Honeycutt Software designs, builds, and runs AI systems for businesses: agents, LLM features, and workflow automation, with the evals, guardrails, and integrations that make them dependable.

Start a project → What we build
invoice-intake · agent run example
  1. readap@ inbox · 3 new PDFs✓
  2. extractllm · schema v4 · conf 0.97✓
  3. matchPO lookup · ERP API✓
  4. policyrules + approval limits✓
  5. review1 low-confidence field→ human
  6. postERP · idempotent write✓
evals passing p95 1.9s cost logged per run
15+ yearsbuilding and leading production platforms
ML in productioncomputer-vision models in live identity-verification pipelines
Regulated domainsFinTech, fraud decisioning, and mortgage data
Low latencyreal-time trading systems where milliseconds count

Services

From a working prototype to software your team relies on.

A model is one part of the system. We build the rest of it too: the data access, the interface, the checks, and the way it gets deployed and monitored.

AI agents & workflow automation

Agents that do real work inside your systems. They read the inbox, fill the form, call the API, and hand off to a person when they aren't sure.

tool usehuman-in-the-loopbackground jobs

LLM features in your product

Search over your documents, copilots, summarization, and structured extraction, built into the product your customers already use.

RAGstructured outputstreaming UI

Evals & reliability

Test suites for model behavior, regression tracking when prompts or models change, guardrails, and cost and latency budgets.

MCP & integrations

Connect models to your tools and data through MCP servers and well-scoped APIs, with auth and audit trails.

Product engineering

Web apps, internal tools, and modernization. Replace the spreadsheet, the shared login, and the script on someone's laptop.

Selected work

An AI product, in production.

Designed, built, and operated end to end: the models, the data pipeline, the web app, the browser extension, and the billing.

Case study · getjobseek.com ↗

JobSeek

An AI job-search assistant. It scores listings against a candidate's resume, writes the tailored application, and keeps the search moving.

  • Resume to profile. PDFs are parsed into structured data the rest of the system can reason over.
  • Match scoring. Every listing is scored against the candidate before anyone spends time on it.
  • Applications written. Cover letters, tailored resumes, and interview answers in STAR format.
  • Runs on a schedule. Jobs are pulled from multiple boards, de-duplicated, scored, and emailed twice a day.
GeminiNext.jsPostgresChrome extensionMCPStripe
getjobseek.com
The JobSeek homepage
  • Web appDashboard for resumes, tracked jobs, and generated applications
  • Browser extensionOne-click analysis on any job board, from LinkedIn and Indeed to Greenhouse and Lever
  • MCP serversLets AI agents search, score, and track jobs directly
  • CLIScriptable search and the automated scoring pipeline

Process

Small steps, real data, early answers.

We find out quickly whether a model can do the job, and we show you the numbers before anything goes to production.

  1. Find the workflow

    Pick one process where AI pays off, and define what "good" means before writing code.

  2. Prototype on real data

    A working version against your actual documents and systems, with an eval set to measure it.

  3. Harden it

    Guardrails, fallbacks, human review, observability, and cost controls. The unglamorous part.

  4. Ship and run

    Deploy, monitor, and improve it, or hand it to your team with the docs and tests to own it.

How we build

Production AI is mostly engineering.

Prompts are easy to demo and hard to trust. These rules are what make the difference.

  • 01

    Evals before demos

    Every AI feature ships with a test set that shows how often it's right, and catches it when that changes.

  • 02

    People stay in the loop where it counts

    Low-confidence and high-stakes cases go to a person. The system knows which is which.

  • 03

    Boring infrastructure

    Queues, retries, idempotent writes, logs, and alerts. The model is new, the reliability patterns aren't.

  • 04

    Model-agnostic

    We use the right model for the task and keep it swappable, so a better or cheaper model is a config change.

  • 05

    You own it

    Code, prompts, eval sets, and infrastructure live in your accounts, with docs your team can work from.

ClaudeOpenAIModel Context ProtocolTypeScriptPythonNext.jsPostgrespgvectorCloudflare WorkersVercelAWS
Shawn Mitchell
Shawn Mitchell · Founder

Who you work with

You work directly with a senior engineer who has run ML in production, led teams through an acquisition, and built systems where downtime costs money.

Shawn Mitchell, former VP of Engineering at Incode / AuthenticID

  • Incode / AuthenticIDVP of Engineering

    Led engineering through an acquisition. Owned delivery of a cloud-native fraud-prevention decisioning platform and directed packaging of ML computer-vision models into live production pipelines.

  • Tradition North AmericaEngineering

    Built low-latency trading systems.

  • Mortgage operationsPrincipal engineering consultant

    Data platforms for mortgage operations.

Have a workflow that should be software?

Tell us what it is and where it hurts. We'll tell you plainly whether AI is the right tool for it.

shawn@honeycuttsoftware.com →