AI & Automation

Production AI engineering and automation

Paul Titov & Co is an 11-person product engineering and AI studio that adds production assistants, RAG, document processing, and workflow automation to new and existing products. We define the job, evaluation, permissions, fallbacks, and cost controls before choosing a model; if rules or conventional search solve the problem better, we say so.

AI is useful when it changes the economics or capability of a real workflow. The starting point is therefore not a model or a chatbot interface, but the task: what people are trying to do, which context the system needs, how success can be evaluated, and where human judgement must remain.

We design and implement assistants, retrieval systems, document pipelines, tool-using agents, semantic search, and structured automation. The work includes the less visible production layer: data preparation, permissions, context engineering, evaluation, fallbacks, observability, latency, and cost control.

AI capabilities can be added to an existing product without rebuilding the platform around them. When a conventional rule, search index, or deterministic workflow is the better tool, we will say so; reliability matters more than attaching AI to every step.

RAG, agents, and document workflows

Retrieval-augmented generation work includes source ingestion, parsing, chunking, metadata, permissions, embeddings, vector search, reranking, citations, and interfaces that let users inspect the supporting context. We use model APIs from OpenAI, Anthropic, and Google alongside pgvector and conventional databases or search indexes.

Agentic systems can use tool or function calling to query internal data, update business systems, create structured output, and coordinate multi-step workflows. Guardrails, permission boundaries, confirmation steps, retries, timeouts, and audit history are designed around the consequences of each action.

Document automation can cover classification, extraction, comparison, summarisation, routing, and human review. Evaluation sets measure accuracy and failure modes against representative work rather than selected demonstrations.

Integrating AI into existing products

We connect AI features to existing APIs, databases, identity systems, analytics, support tools, and user interfaces. The surrounding software engineering and data analytics work is often as important as model selection. For products where AI is the central customer value, see our AI product development experience.

Where AI earns its place

We start with the job, not the model. A support assistant should reduce resolution time without inventing policy; a document workflow should extract the fields people actually need and show where confidence is low; an internal copilot should use the organisation’s permissions rather than expose everything it can retrieve. Together with the client we define the source material, permitted actions, quality threshold, human review, fallback behaviour, and cost per completed task.

The production system usually includes much more than a prompt: ingestion and document parsing, retrieval, model routing, structured outputs, tool permissions, queues, evaluation sets, audit logs, observability, and an interface for correction. We test representative and adversarial cases before launch, then measure quality, latency, usage, and cost in operation. Models can change; the client’s data model, evaluation process, and operational controls remain durable.

Questions teams ask before adding AI

Can you add RAG or an assistant to an existing Laravel or Node.js product without rewriting it?

Yes. We normally connect the AI capability through explicit application services, queues, APIs, and the existing identity model. We first inspect the codebase, data ownership, permissions, and release process; a rewrite is proposed only when a specific constraint makes incremental integration unsafe or disproportionately expensive.

Which model do you use: OpenAI, Anthropic, Gemini, Azure, Mistral, or a local model?

The task and operating constraints decide. We compare output quality, latency, data policy, deployment requirements, availability, and cost against a representative evaluation set. The architecture can route between models or replace one later, so a product decision does not become an unnecessary dependency on a vendor.

Will you recommend AI for every automation project?

No. Deterministic rules, full-text search, a database query, or a conventional workflow are often cheaper and more reliable. We use a model where language, ambiguity, classification, extraction, or generation creates measurable value, and keep deterministic controls around actions that require certainty.

How do you keep an assistant from exposing confidential information or taking the wrong action?

The assistant inherits product permissions instead of receiving unrestricted access to a knowledge base. Retrieval is filtered by identity and source permissions; tool calls use narrow scopes, validation, confirmation, audit logs, and fallbacks. High-consequence actions remain subject to explicit approval or a human review queue.

What has to exist before an AI feature goes into production?

We require representative evaluation cases, defined failure behaviour, permission checks, cost and latency limits, observability, and a way for users or operators to report and correct bad output. A polished demo is not evidence that the system is ready for real data, unusual inputs, provider failures, or sustained usage.