AI that does real work

AI Agents, Chatbots & LLM Integrations.

AI wired into real business systems — support agents grounded in your own data, permissioned actions, human handoff, and a full audit trail of everything it did.

4.9/5 · 580+ reviews|Top Rated · Fiverr & Upwork|800+ orders · 10+ yrs

Start a project What you get

Demos are easy, production is not

Building a chatbot that answers questions impressively takes an afternoon. Building one that touches billing, tickets or inventory without occasionally doing something expensive and wrong takes rather more care.

The difference is architecture. Every action an agent can take should be an explicit, permissioned, logged operation — never freeform access to your database. Anything destructive should require approval until you trust it. And every run should leave a trace you can read afterwards.

That is how I build AI features: grounded in your real data, tightly scoped, honest about uncertainty, and with a human in the loop wherever the cost of being wrong is high.

What I build

AI work.

Support agents

Grounded in your knowledge base and ticket history, with confidence-based handoff to a human.

Sales & pre-sales chat

Answers product and pricing questions from your real catalogue rather than inventing plausible details.

Workflow agents

Multi-step business processes with explicit tool calls, dry-run mode and approval gates.

Document processing

Extraction and classification from invoices, forms and email, with confidence scores and review queues.

Retrieval over your data

RAG pipelines over your documentation, tickets and product data, with citations back to the source.

Integration into your stack

Wired into WordPress, your CRM, your helpdesk, WhatsApp or wherever your customers actually are.

The detail that matters

How I approach it.

Ground it, or it will make things up

A model asked about your product without access to your product data will produce a confident, plausible, wrong answer. Retrieval over your actual documentation, with citations, is what turns that into something you can put in front of customers. When it does not know, it should say so.

Every action explicit and logged

Agents should not have database access. They get a defined set of operations — issue a refund, apply a credit, escalate a ticket — each with its own permission, validation and log entry. That way the worst case is a wrong call on a bounded action, not an unbounded one.

Human handoff is a feature, not a fallback

The measure of a good support agent is not how many conversations it handles alone, it is how gracefully it hands over the ones it should not. Confidence thresholds, explicit escalation triggers and full context passed to the human make the difference between a helpful agent and an infuriating one.

Cost and latency are design constraints

Token cost and response time are real product constraints, not implementation details. Model choice per task, caching, prompt size discipline and streaming responses are what keep an AI feature affordable and pleasant to use at volume.

How we work together

The process.

Listen

A 30-minute call to understand the problem behind the brief. The fix is often not what you first ask for.

Quote

A real number tied to a real scope — never a copy-paste price list. Sent within 24 hours.

Build

I work in staging, in your repo, with commits you can audit. I send a daily update so you are never wondering.

Deliver

Smoke test together, document, deploy. Then I stick around to catch the edge cases.

What you get with me.

Retrieval grounded in your own data with citations
Explicit, permissioned, logged tool calls
Dry-run and approval modes for destructive actions
Confidence-based human handoff with full context
Full run tracing for audit and debugging
Cost and latency budgets, measured in production

Questions, answered.

Which models do you use?

Whatever fits the task and your constraints — the current Claude models are a strong default for reasoning and tool use, and I will use a smaller, cheaper model for classification-type work where it performs just as well. I will also tell you where a non-AI solution is simply better.

Will it hallucinate about my products?

Grounded retrieval with citations dramatically reduces it, and the agent should decline rather than guess when it lacks information. I test explicitly for this before anything goes in front of customers.

Is my data used to train models?

Not with the API tiers I use, which do not train on submitted data. I configure retention settings deliberately and document exactly what is sent where.

How much does an AI feature cost to run?

It depends on volume and model choice, and it is estimable in advance. I model expected cost per conversation before building, so you can decide whether it makes commercial sense.

Can it handle WhatsApp or live chat?

Yes — WhatsApp Cloud API, website chat widgets, or your existing ticket system. Meeting customers where they already are matters more than the channel being new.

Need this built?.

Tell me what you need. I send a real quote based on your specific project — never a fixed price for a problem I have not heard.

Start a Project Contact

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