Custom AI Assistants

// the problemThe Problem

Why off-the-shelf doesn't work

Most teams try ChatGPT or Copilot, get excited for a week, and then stop using it. The reason is always the same: generic tools don't know your data, your processes, or your constraints. They can't pull from your internal docs, they can't trigger actions in your systems, and they forget everything between conversations.

A custom assistant is different. It knows your domain. It connects to your tools. It remembers context. And it does the specific things your team needs — not everything for everyone.

// what these look likeWhat These Look Like

The spectrum

Every assistant I build is different, but they tend to fall somewhere on this spectrum. Most clients start simple and expand once they see what's possible.

◆ Task bots simple

Single-purpose assistants that do one thing well. Summarize meeting notes, draft responses to common emails, extract data from documents, answer questions about internal policies. Fast to build, immediate ROI.

◆ Workflow assistants moderate

Assistants embedded in a specific workflow with tool access — they can read from your database, call your APIs, pull from your knowledge base, and take actions on your behalf. Think: an assistant that triages incoming support tickets by reading the ticket, checking the customer's account, and drafting a response with the right context.

◆ Multi-agent systems advanced

Multiple specialized agents that coordinate on complex tasks — a research agent that gathers information, an analysis agent that interprets it, and a drafting agent that produces the output. Persistent memory across sessions, sophisticated tool orchestration, human-in-the-loop checkpoints where it matters.

// examples of what I've builtExamples

This looks like

Internal knowledge assistant

RAG-powered assistant over a company's internal docs, SOPs, and Confluence pages. Employees ask questions in natural language, get accurate answers with source citations, and the system tracks which questions it can't answer so the docs team knows what to write next.

Email-to-data extraction pipeline

Ingests unstructured emails containing operational data, extracts structured fields using LLM-powered parsing, validates against known schemas, and pushes clean data into the client's system of record — replacing hours of manual data entry per day.

Code review agent

Integrated into the PR workflow via GitHub. Reviews code changes against the team's style guide and architecture patterns, flags potential issues, and leaves inline comments — functioning as a tireless first-pass reviewer that catches the things humans skim past.

QA & compliance automation

Pulls call recordings, transcribes them, and evaluates each call against a compliance questionnaire — producing a scored report with evidence citations. Turned a full-time manual QA process into a review-and-approve workflow.

// under the hoodHow It Works

How these are built

I don't use no-code AI builders or drag-and-drop platforms. These are real software systems — version controlled, tested, monitored, and designed to run reliably in production.

Model layer

The right model for the job — not always the biggest one. GPT-4o for complex reasoning, Claude for long-context work, smaller models for classification and extraction where latency and cost matter. Self-hosted inference when privacy requires it.

Tool & integration layer

MCP servers, function calling, API integrations — the assistant connects to your existing systems rather than living in a silo. Database reads, Slack notifications, CRM updates, document generation — whatever the workflow requires.

Memory & context

RAG over your knowledge base, conversation memory that persists across sessions, user-specific context that improves over time. The assistant gets smarter the more your team uses it.

Evaluation & monitoring

Every assistant ships with quality monitoring — so you know when it's giving good answers and when it's drifting. Automated eval harnesses, cost tracking, latency dashboards, and alerting for production issues.

Human-in-the-loop

For high-stakes outputs, the assistant drafts and a human approves. I design the review interface and the escalation logic so your team stays in control without doing all the work.

// how we work togetherWorking Together

The process

Scoping call

30 minutes, free. You describe the workflow you want to augment, I ask questions about your data, systems, and constraints. We figure out if this is a good fit and what the right starting point is.

Technical spec & proposal

I write a short technical document covering what the assistant will do, how it connects to your systems, what models and infrastructure are involved, and a realistic timeline. You review, we adjust, and agree on scope.

Build & iterate

I build in short cycles with working demos along the way — not a big reveal at the end. You and your team use the assistant early, give feedback, and we refine until it does what you actually need.

Deploy & hand off

The assistant goes into production with monitoring, documentation, and a handoff to your team. I stick around for a support window to make sure everything runs smoothly — and I'm available for ongoing iteration if you need it.

// pricingPricing

What this costs

Every build is different, so I scope and price each project individually based on complexity, integrations, and timeline. A focused task bot is a short engagement; a multi-agent system with deep integrations is a larger one.

The scoping call is free and there's no obligation. I'll give you an honest assessment of what your assistant needs, what it would take to build, and whether it's worth building at all.

// common questionsCommon Questions

Frequently asked

Q: Do I need to provide training data?

Not usually. Most assistants are built on top of foundation models (GPT-4, Claude, etc.) with your domain knowledge supplied via RAG — your existing docs, knowledge bases, and databases. Fine-tuning with custom training data is an option for specific use cases but is rarely the right starting point.

Q: What about data privacy?

I build with privacy as a constraint from day one. That can mean self-hosted models, zero-retention API agreements, data anonymization pipelines, or HIPAA BAAs with cloud providers — whatever your compliance requirements demand. I've done this in regulated healthcare environments and can sign NDAs and BAAs.

Q: Can my team maintain this after you leave?

Yes — that's the goal. Everything is documented, version controlled, and built with standard tools your engineers already know (TypeScript, Python, PostgreSQL). I'm not building a black box that only I can modify. I also offer ongoing retainer support if you want me available for iteration and maintenance.

Q: How long does a typical build take?

A focused task bot can be working in 1–2 weeks. A workflow assistant with integrations is typically 4–8 weeks. Multi-agent systems vary more widely depending on scope. I'll give you a realistic timeline in the proposal — I'd rather set honest expectations than overpromise.

Q: What if I'm not sure what kind of assistant I need?

That's what the scoping call is for. You describe the workflow that's painful, I help figure out what an assistant could realistically do about it, and we go from there. Sometimes the answer is "you don't need an assistant, you need a better pipeline" — and I'll tell you that too.

// other ways I can helpOther Ways I Can Help

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