/AI Services
I help startups and businesses build production-ready AI systems that make knowledge easier to access, automate repetitive workflows, and turn AI ideas into real products.
AI is only worth building when it removes real friction. These are the problems I’m usually brought in to fix.
Your team loses hours searching across documents, policies, and internal knowledge for answers that should take seconds.
People manually process the same emails, forms, PDFs, and routine tasks every day.
You want to introduce AI, but don’t have the engineering expertise in house to deploy it reliably.
You need an AI-enabled MVP without assembling a large engineering team first.
Four ways I help teams put AI to work, each scoped around a clear business problem.
Give your team fast, grounded answers from your own documents, instead of another search box nobody trusts.
Take repetitive document and information-processing work off your team’s plate, with people still in control of what matters.
Run defined, multi-step workflows with AI — inside clear boundaries, with safeguards and an audit trail.
Turn an AI idea into a working product your users can actually log into, without hiring a full team first.
Production AI systems I’ve built end to end. Client details are limited to what’s already public.
Voice AI for Clinical Documentation
Doctors lose a large part of every visit to writing documentation instead of focusing on the patient.
Constrained, verified output where a wrong drug name or dosage is not acceptable. Live in production.
Agentic Marketing Platform for E-Commerce
E-commerce brands spend hours manually monitoring scattered store and ad data and running campaigns.
Agents that turn messy multi-source data into useful actions, with nothing running without sign-off.
Peer-to-Peer Lending Marketplace
Renting and lending between strangers had no trusted way to handle payments, security deposits, and returns.
End-to-end product ownership — from database schema to payments to deployment. Live in production.
A simple, transparent process. Scope and timelines depend on the complexity of the project, and are agreed before any build starts.
Understand the business problem, current workflow, constraints, and desired outcome.
Define deliverables, timeline, integrations, risks, and acceptance criteria.
Develop, test, and validate the solution, with regular communication throughout.
Deploy the solution, document it, and agree on any ongoing support.
Every project is quoted on scope, complexity, and business value — no one-size-fits-all pricing.
For teams that need clarity before committing to implementation.
A defined use case, proposed architecture, scope, risks, and implementation plan.
For a clearly defined AI feature, workflow, or proof of concept.
An agreed implementation with explicit acceptance criteria.
For businesses ready to deploy and improve an AI system.
A scoped production implementation, with ongoing support available by agreement.
The model is one piece. I also build the app, the API, and the data layer around it.
Experience shipping real products across frontend, backend, and cloud deployment.
We start from the workflow you want to improve, not from the newest model.
Agreed scope, acceptance criteria, and maintainable code your team can own.
One engineer accountable from first conversation to production release.
Knowledge assistants over internal documents, AI workflow automation, custom agents for defined multi-step tasks, and AI-powered products and MVPs. If your idea doesn’t fit neatly into one of these, a discovery call is the quickest way to find out.
Yes. I can work within your existing codebase, APIs, and databases, or build a separate service that your application calls.
Yes. Knowledge assistants are built specifically around your own documents, and access can be restricted so people only retrieve what they’re permitted to see.
We agree on data-handling boundaries during scoping: what data is used, where it is stored and processed, which model providers are acceptable, and who has access. I follow your security requirements and am happy to work under an NDA. I don’t hold formal compliance certifications, so if your project has specific regulatory requirements, we’ll identify them early and design around them with your team.
It depends on scope and complexity. A discovery and scoping engagement is short; a focused sprint or proof of concept is typically measured in weeks; production builds take longer. You’ll get a timeline as part of the scope before any build starts.
Yes. I work remotely and schedule calls and updates around overlapping hours with clients in the US, UK, Europe, and the Gulf.
We talk through the problem you want to solve, your current workflow, constraints, and what success would look like. You’ll leave with a clear view of whether AI is a good fit and what a sensible next step would be.
Yes, by agreement. Ongoing support, monitoring, and improvements can be scoped after deployment based on what your system needs.
Tell me what you want to improve, automate, or build. We can discuss the use case and determine the right next step.