AI Integration & Engineering

Leverage AI to bring your business into the future.

That's the promise on every pitch deck — here's how it actually gets built. I wire models into the systems you already run, stand up air-gapped local models where data can't leave, and put frontier APIs behind cost ceilings where they're the right call. AI as engineering, held to the same bar as everything else Tall Karol ships.

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AI where it earns its place.

A model enters a project the way any other dependency does — when a specific capability solves a specific problem.

The bar

Held to production, not to the demo.

A model in your business is a dependency like any other — so it ships with the same discipline as any other. If it can't survive real data, real volume, and real failure modes, it doesn't ship.

  • Error handling that assumes the model will sometimes be wrong
  • Evals run on your data before launch, not after complaints
  • Cost ceilings and fallbacks like any other dependency

The honest no

What I won't sell you.

AI is an ingredient in how I build, not the pitch. It enters a project when a specific capability solves a specific problem — and some proposals deserve a no before they cost you a quarter.

  • A chatbot bolted on the side of your website
  • An AI rebrand of software that didn't need a model
  • Local-versus-hosted as ideology — it's an engineering decision

AI in your existing systems

Models built into the software your team already runs.

The fastest way to get value from AI isn't a new platform — it's a new capability inside the systems you already trust. I wire LLM steps into your current stack the way I wire any other integration: through APIs, webhooks, and middleware, with error handling that assumes the model will sometimes be wrong.

  • Plain-English search over your records, documents, and history
  • Document intelligence in the intake queue — extraction, classification, summarization
  • Transcription and enrichment feeding the pipeline you already run
new platforms for your team to learn
Zero
with a new capability inside it
One system

Internal AI networks

Air-gapped, local, and entirely yours.

For work where data can't leave — contracts, records, customer data — I deploy open-weight models on your own infrastructure and build the internal tools around them. An AI network that runs inside your walls: air-gapped when compliance requires it, private by architecture rather than by policy.

  • Open-weight models deployed on hardware you control
  • Internal tools — search, intake, reporting — wired to the model inside the boundary
  • No third-party API in the loop, no per-token bills
when compliance requires it
Air-gapped
predictable cost at scale
No per-token bills

API-based tools

Frontier models, engineered like production dependencies.

Plenty of jobs are better served by a frontier model behind an API. I build the tools your business needs on top of them — and treat the model like any other production dependency: cost ceilings, fallbacks, monitoring, and evals before your customers ever see an answer.

  • Custom tools for the jobs frontier models do best — drafting, reasoning, analysis
  • Cost ceilings, fallbacks, and monitoring wired in from day one
  • Evaluated against your real data before launch — not a happy-path demo
API spend, enforced in code
Capped
against your data before launch
Measured

Available on retainer or by project — same discipline either way.

How retainers work →How projects work →

Everything on this page is bespoke engineering — AI built into the systems you already run. Want a network of specialized assistants instead? TALLKAROL HiveMind is a managed network — API-hosted on your own Anthropic account or built locally — on subscription, human-gated, with an autonomous package for orchestration and parallel coordination.

Meet the HiveMind →

From audit to handoff — the pilot earns the build.

  1. Step 1

    Audit

    Map where the hours actually go — which workflows a model can carry, and which it can't.

  2. Step 2

    Pilot

    One workflow, real data, production-like conditions. Measured, not demoed.

  3. Step 3

    Production

    Error handling, evals, cost ceilings, fallbacks — shipped like any other system.

  4. Step 4

    Handoff

    Runbooks and docs so your team can run it — and a straight read on what's worth building next.

Tooling
  • Claude & OpenAI APIs
  • Open-weight models (Llama, Mistral)
  • Ollama / vLLM
  • RAG & vector search
  • PostgreSQL + pgvector
  • Whisper transcription
  • Next.js
  • TypeScript
  • AWS

What these builds look like, end to end.

Nothing here is a link — AI work lands inside systems that aren't public. So the wiring is the sample: what connects to what, what runs where, and where a person stays in the loop.

Document intelligence · Inside your existing stack

Intake that reads its own paperwork.

Orders, invoices, and specs arrive as PDFs and email attachments and get keyed in by hand. A model extracts the fields, classifies the document, and writes the result into the system your team already works in.

  1. Inbox
  2. Extraction
  3. Validation
  4. Your CRM

In the loop: Low-confidence extractions queue for a person instead of committing a guess.

Retrieval · Plain-English search

Search that answers, with the receipts.

Years of records, tickets, and documents nobody can find twice. Indexed as embeddings, searched in plain language, and answered with the source rows attached — so an answer can be checked rather than trusted.

  1. Your records
  2. Embeddings
  3. Retrieval
  4. Cited answer

In the loop: Every answer ships with what it was drawn from, so a wrong one is visible in seconds.

Local models · Inside your walls

An assistant that never leaves the building.

For work where the data can't go to a third party at all: open-weight models on hardware you control, with the internal tools — search, intake, reporting — wired to them inside the boundary.

  1. Your hardware
  2. Open-weight model
  3. Internal tools

In the loop: No third-party API in the loop, no per-token bill, and no terms to renegotiate later.

How pricing works.

Three ways to engage, and a fixed number agreed before any of them starts — no hourly meter, and no open-ended AI budget that grows every time someone has an idea.

Audit

Fixed fee

A read on where a model could actually carry work — which of your workflows hold up under real data, which don't, and what local-versus-hosted means for yours specifically.

  • Workflows ranked by what a model can realistically carry
  • A straight answer on local, hosted, or both
  • Yours to keep — whoever ends up building it

Project

Fixed price, milestone-based

A defined build, starting with a paid pilot on one real workflow — typically 2–4 weeks. The production build is scoped and priced once the pilot has earned it: 4–8 weeks, or 8–12 for a full internal network on local models.

  • A pilot measured on your data, not demoed on mine
  • Error handling, evals, cost ceilings, and fallbacks in scope
  • Runbooks, docs, and handoff included

Retainer

Monthly

Standing engineering capacity once a model is in production — because the models change under you, and so does what's worth handing them.

  • A predictable monthly block of work
  • Monitoring, evals, and cost kept honest as volume grows
  • Cancel or pause between months

What moves the number

Bring the workflow to a 30-minute call and you'll leave with a real range — not a proposal you have to chase.

  • How many workflows the model has to carry
  • Whether it runs local, hosted, or both
  • Hardware, when the models run inside your walls
  • What it has to read from — and write back to

Frequently Asked Questions

Can you add AI to the systems we already use?

Yes — that's the default posture. An LLM step goes into your existing stack the way any integration does: through APIs, webhooks, and middleware. Your team keeps the tools they know; the tools get new capabilities — search that understands plain English, intake that reads documents, records that enrich themselves.

What is an air-gapped AI deployment?

A setup where the model runs entirely on infrastructure you control — your servers or your private cloud — with no route to the outside world. Open-weight models make this practical: your contracts, records, and customer data never touch a third-party API, and there are no per-token bills. It's the right architecture when compliance or confidentiality makes 'we promise not to look' insufficient.

Should we run local models or use a hosted API?

It's an engineering decision, not an ideology. Data sensitivity, volume, latency, and the capability the job actually needs all weigh in. Plenty of workloads are better served by a frontier model behind an API with cost ceilings and fallbacks; some can never leave your walls. Many businesses end up with both — routed by task.

Is our data used to train anyone's models?

With local models, the question doesn't arise — nothing leaves your infrastructure. With hosted APIs, I wire providers under terms that exclude training on your data, and the integration sends only what each task needs rather than whole records by default.

Do we need a chatbot?

Usually not. The AI features that hold up in production mostly don't look like chat — they look like better software: search that finds the thing, intake queues that read their own paperwork, reports that write their first draft. If a conversational interface genuinely fits your workflow, I'll build it — but it's an interface choice, not the goal.

How long does an AI integration take?

A pilot on one real workflow typically lands in 2–4 weeks. A production integration runs 4–8; a full internal AI network on local models 8–12, depending on hardware. Either way you get a blueprint with milestones before I write code — and the pilot has to earn the production build.

Bring the workflow, not the buzzword.

30 minutes. Tell me where the hours go — the intake queue, the paperwork, the search that never finds anything — and you'll leave with a straight read on whether a model can carry it, and whether it should run local or hosted.

Book an intro call