AI Integration Services.

Most businesses do not need a new AI product. They need the product they already sell, or the system their team already uses, to do one or two things better: read an inbound document and fill in the form, summarise a long thread, find the right record from a vague description, or draft the first version of a reply.

AI integration is the engineering that makes that happen inside your codebase. It covers the call to the model, but most of the work sits around it: getting the right data into the prompt, forcing the output into a shape your code can trust, handling the slow and failed calls, keeping costs predictable, and proving the feature is accurate before real users see it.

It is a different job from an autonomous agent or a customer chatbot. If you need software that takes multi-step actions on its own, see AI agent development. If you need a conversational assistant on your website or WhatsApp, see AI chatbot development. If the feature must answer from your own documents with citations, that is RAG development, which we often build as part of an integration.

Document and data extraction

that turns invoices, contracts, CVs, emails and PDFs into validated fields your database can store, with a human review step for low-confidence results

Summaries and drafts

of long records, call notes, tickets and threads, written in your product’s tone and kept inside the screen where the user already works

Semantic search and matching

that finds records by meaning rather than exact words, built on embeddings in PostgreSQL with pgvector or a vector database such as Pinecone

Classification and routing

that tags, scores and routes inbound leads, tickets and documents so the right person sees them first

In-app copilots

that help a user fill a form, write a listing or configure a setting, with every suggestion editable before it is saved

Content generation inside your platform

such as product descriptions, social captions or proposals, metered per account so heavy users cannot run up your bill

1. 01Pick one feature with a measurable outcomeWe start with a single feature where success can be counted: minutes saved per document, search success rate, or proposals sent. A feature nobody can measure is a feature nobody can defend at renewal.
2. 02Build the evaluation set firstBefore writing prompts we collect 50 to 200 real examples with the answer you would accept. Every model, prompt and setting is scored against that set, so changes are judged on evidence, not on a good-looking demo.
3. 03Design the data pathWe decide what context the model needs, where it comes from, and what must never leave your systems. Personal data is minimised or masked, and enterprise API endpoints are used so your data is not used for training.
4. 04Use structured outputs, not free textThe model returns JSON that matches a schema, using the providers’ structured output and tool calling features. Your code validates it like any other input and rejects what does not fit.
5. 05Add guardrails and fallbacksTimeouts, retries, a cheaper fallback model, content filters and a clear message when the AI cannot help. The rest of your product keeps working if a provider has a bad hour.
6. 06Ship behind a flag, then watch itWe release to a slice of users first and log every call: prompt version, model, latency, tokens, cost and user feedback. That LLMOps layer is what lets you improve the feature after launch instead of guessing.

We are model-agnostic. The right model is the one that passes your evaluation set at an acceptable cost and latency, and that changes every few months, so we keep the provider behind one interface in your code.

Models and APIs

The OpenAI API with structured outputs, Anthropic’s Claude API with tool use, Google Gemini and DeepSeek where they fit, and open-weight models in your own cloud when data residency requires it

Application layer

Laravel and Django services on the backend, the Vercel AI SDK for streaming responses into React and Next.js front ends

Retrieval

Embeddings in PostgreSQL with pgvector, or Pinecone at larger scale, with hybrid keyword and vector search

Jobs and scale

Queued AI calls on Laravel Horizon or Celery with Redis, so slow model calls never block a web request

Quality and cost

Evaluation sets in the repository, prompt versioning, token and cost logging per tenant, and per-account usage limits or credits

AI integration projects typically start at $6,000 for one production feature added to an existing product. That covers discovery, the evaluation set, the integration itself, guardrails, logging and a release behind a feature flag.

The price rises with the number of features, the state of the data the feature needs, whether retrieval over your documents is required, and how strict the review and compliance steps are. A second feature on the same foundation usually costs much less than the first, because the provider layer, logging and evaluation harness are already in place.

Running costs are separate and paid to the model provider. We estimate them per user or per document before we build, choose the cheapest model that passes your evaluation set, and add caching and usage limits so the monthly bill stays predictable.

A single, well-defined feature usually reaches production in three to six weeks: one week for discovery and the evaluation set, two to four weeks to build and test, and a staged release. Features that need document retrieval, a new admin screen or a security review take longer, and we say so in the scope before work starts.

Some problems look like AI problems and are not. If the rule can be written down, write it down: deterministic code is cheaper, faster and always gives the same answer. In Prozilio, the job score a freelancer sees for each Upwork post is calculated from hire rate, competition and client history with no AI call at all, so it costs nothing per job. The model is used where judgement and language are needed, in writing the proposal.

We will also tell you when your data is not ready, when the accuracy you need is higher than any model can reach today, or when the running cost would exceed the value of the feature. Those answers save more money than any build.

We run AI in our own products

Prozilio, Quevix and Agenso all ship LLM features to paying users, so we have paid the token bills and handled the edge cases ourselves.

We work in your stack

Laravel, Django, Node.js, React and Next.js are our everyday tools, so the AI feature fits your codebase instead of living in a side service nobody maintains.

You own everything

The code, prompts, evaluation sets and provider accounts are yours from day one.