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Service · AI Engineering

AI engineering that ships features your customers pay for, inside the product you already have.

I add production AI features to your existing SaaS, from RAG over your customers' documents to AI workflow automation, with evaluation and guardrails built in from day one.

What is AI engineering for a SaaS product?

AI engineering is the work of turning a model into a reliable product feature. It covers retrieval, prompts, data access, cost, evaluation and failure handling, inside your real codebase.

The goal is a feature customers use and pay for, not a demo that only works on the happy path.

Who is this for?

This is for B2B SaaS founders with a working product and an AI feature on the roadmap that has not shipped yet. It fits teams on .NET, Node or similar web stacks who want the feature built in their own repo.

What do you build?

I build across the five modules below, scoped to the one feature that matters most to your customers first.

  • RAG systems

    Question answering over your customers' documents, with tenant isolation and cited sources.

  • AI features in existing apps

    AI features built into your current codebase and data model, not a separate demo app.

  • AI workflow automation

    Model-driven steps inside existing workflows, such as triage, extraction, drafting and review.

  • LLM integration architecture

    A provider-agnostic model layer, prompt management, cost controls and structured outputs.

  • Evaluation and guardrails

    Test sets, quality checks and fallbacks so you know when an AI feature is good enough to ship.

How does an engagement start?

Every engagement starts with the AI Feature Sprint: two weeks, fixed scope, fixed price. I pick one feature with you, build a working version against your real data, and measure it against an agreed test set. You then decide whether to take it to production with me.

What proof is there?

I built LogiqAIAssist, a multi-tenant document Q&A product on RAG, pgvector and Microsoft.Extensions.AI, and I am building LogiqReview AI, an AI code review assistant. Both case studies show the architecture and the decisions behind it.

AI Engineering: frequently asked questions

Can you add AI features to an existing .NET or web application?

Yes. That is the main focus. I build the feature inside your current codebase and data model, following your architecture, instead of creating a separate demo app.

Which AI models do you use?

I design a provider-agnostic model layer, so you can use the provider that fits your cost, privacy and quality needs, and switch later without rewriting the feature.

How do you keep customer data safe in a RAG system?

Retrieval is scoped per tenant, so one customer's documents can never appear in another customer's answers. Data handling and model provider choices are agreed in writing before the build.

How do you know the AI feature is good enough to ship?

We agree a test set and quality bar during the AI Feature Sprint. The feature is measured against it before production, and the same tests run after every change.

What happens if the AI cannot answer a question?

The feature is designed to say it does not know, or to hand off to a person, instead of guessing. Answers that use your documents cite their sources.

What does an AI Feature Sprint deliver?

In two weeks you get one working AI feature in your repository, an evaluation against the agreed test set, and a production plan with a cost estimate.

Start with the AI Feature Sprint

2 weeks, fixed scope, starting from [PLACEHOLDER: USD price]. Tell me about your product and I will reply with next steps.

Book the AI Feature SprintSee pricing