AI-powered products

AI product development

Paul Titov & Co designs and builds AI-powered products for companies that need assistants, RAG, document intelligence, semantic search, or workflow automation to perform a measurable job. We build the evaluation, permissions, citations, fallbacks, interfaces, and operations around the model rather than shipping an AI-wrapper demo.

An AI-powered product is not defined by which model appears in its architecture diagram. It is defined by the job it performs, the context it can use, the reliability users can expect, and the workflow around uncertain output. We build products where assistants, retrieval, document understanding, semantic search, classification, generation, or automation are central to the customer value.

Typical product shapes include AI SaaS, internal knowledge platforms, customer and employee assistants, document intelligence, AI search, support automation, and specialised tools that combine models with proprietary data and deterministic software.

RAG, knowledge systems, and document intelligence

Retrieval-augmented generation begins with information architecture. Documents and records have to be collected, parsed, segmented, enriched with metadata, permissioned, embedded, indexed, retrieved, and cited in a way the user can inspect. We develop these pipelines with vector databases such as pgvector, conventional search, reranking, structured data, and source-aware interfaces.

Document systems can classify content, extract fields, summarise cases, compare versions, route work, and generate structured outputs for downstream software. The design accounts for low-confidence results, human review, traceability, and the difference between a useful suggestion and an automated decision.

Evaluation and production AI engineering

Production quality depends on evaluation, not prompt intuition. We define representative tasks and expected behaviours, test retrieval and model output, monitor failure classes, and make model, prompt, context, and workflow changes against evidence. Latency, token cost, rate limits, fallbacks, privacy, permissions, and observability are treated as product requirements.

Our AI and automation service covers model integration, agents, tool calling, structured outputs, and AI features inside existing software. Software engineering, data analytics, and product design complete the system around the model.

When a rule, database query, search index, or conventional workflow is more reliable than a model, we use it. AI is one component of the product architecture, not an excuse to remove engineering discipline.

Building a product, not an impressive demo

AI product work starts by defining what a correct, useful, and unacceptable answer looks like for a particular user. We assemble representative evaluation cases early, including ambiguous inputs, missing evidence, conflicting documents, prompt injection, and actions that require approval. This gives the team a stable way to compare prompts, retrieval strategies, and models instead of judging a few polished examples.

The surrounding product receives equal attention: permissions, source freshness, citations, feedback, human escalation, latency, usage limits, cost controls, and audit history. We can launch a narrow capability first, observe where it succeeds and fails, and expand only when evidence supports the next level of autonomy.

Questions product teams ask about AI products

Is this for a new AI product or an AI feature inside an existing platform?

Both. We can build a new product where AI is central, or add a bounded capability to an established Laravel, Node.js, mobile, or enterprise system. Existing identity, permissions, data ownership, support, and analytics are treated as product requirements, not integration details to solve after the demo.

Who is a good fit for this work?

We work best with product companies and corporate teams that have a real workflow, users, domain knowledge, and someone accountable for the outcome. We are a weaker fit for undifferentiated “AI wrapper” launches whose only requirement is to expose a general model behind a new interface.

How do you decide between RAG, fine-tuning, tools, and ordinary software?

We begin with failure cases and the information or action the task requires. RAG is useful when answers need current governed sources; tools are useful when the system must read or change another system; fine-tuning can help narrow repeated behaviour after evidence exists. Many parts should remain ordinary application logic.

How do you evaluate quality before launch?

We create a versioned set of representative inputs and expected properties, including ambiguous, incomplete, confidential, and adversarial cases. Automated checks, model-based grading where appropriate, and human review are combined with product metrics. The set is rerun when prompts, retrieval, data, or models change.

What happens when the model is wrong or unavailable?

The product needs an explicit degraded state: ask for clarification, cite uncertainty, return a deterministic result, queue human review, or postpone the action. Provider timeouts, rate limits, and cost ceilings are handled as normal operating conditions rather than exceptional surprises.