AI integration,
without a rebuild
Add LLM features and RAG over your internal data to the product you already run — through APIs and middleware, rolled out behind flags. The AI layer stays decoupled from your codebase, first feature in 2–3 weeks.
You already have a product. The question is how to put AI inside it without stalling the roadmap or betting the architecture on a rewrite. This is the integration end of our AI Development practice: adding model features and retrieval to a live stack. For a product where AI is the point, see Generative AI Development; if you want a read on where AI fits before committing, start with an AI Readiness Assessment. For the how-to — approaches, cost, and risks — see our guide to AI integration.
Integration, proven
in production
AI added to products already in production, through APIs and incremental rollout. Senior-built and kept running after the first feature ships.
100+
Production software across fintech, health, and manufacturing, with AI layered into systems already in use.
2–3wks
Wired into your stack and shipped behind a flag.
100%
Senior engineers on every integration; no juniors learning on your stack.
99.9%
Across the products we add AI to and keep running.
Add an AI feature without a rebuild
API-first integration and RAG over your data, by senior engineers.
AI inside the product
you already run
An AI layer that calls into your existing backend, reads from your existing data, and ships one capability at a time, staying decoupled from your codebase as the product gains intelligence.
LLM features via APIs
Model-backed features delivered as a middleware layer your backend calls. Your codebase keeps its shape; the AI sits next to it.
RAG over your data
Retrieval over your documents and records, so answers cite your sources instead of the model's training set.
Feature-flagged rollout
Every capability ships behind a flag. Turn it on for a few users, watch the numbers, then widen — no big-bang release.
Fits your stack
The middleware plugs into the surfaces you already expose: your REST and GraphQL APIs, your auth, your event bus. No new infrastructure to adopt for AI.
Security & tenancy isolation
Retrieval honors the permissions your product already enforces, tenants stay separated, and access is scoped per user.
Observability you can read
Accuracy, cost, and latency tracked per feature, so you can see whether a capability is worth keeping on.
Capabilities you can
switch on
What an existing product gains once an AI layer sits beside it. Pick the capability that moves a metric first, ship it behind a flag, and add the next when the numbers hold.
AI assistant & copilot
A copilot surface inside your product — a side panel, an inline prompt, a slash command — that understands a request in context and helps the user move through the task without leaving the app.
Knowledge assistant (RAG)
Replies that pull from your own documents, tickets, and records, retrieved at query time and scoped to what each user is already allowed to see, so the model cites your sources and cannot reach past them.
In-app agent & automation
A tool-using agent that takes an action inside your product against your existing APIs, pausing for a person on anything that changes state or reaches a customer.
Document & data extraction
PDFs, forms, and email turned into structured fields your systems can use — the model reads the document, your rules check what it hands back.
Semantic & AI search
Search your content by what it means, so a question in plain language returns the right passage even when the words on the page do not match.
Predictive & scoring features
Scoring, routing, and recommendations learned from your own data: a churn flag, a priority queue, a next-best action surfaced where the user already works.
From your stack
to AI in production
Three phases that take a model feature from your existing systems into a flagged production rollout — senior engineers and AI agents working against the stack you already run.
Scope
Map & design
Map systems & data
Walk your services, data sources, and permissions to find where a feature plugs in and what it is allowed to read.
Retrieval & middleware design
Design the API layer and, where the feature needs it, the retrieval path — chunking, embeddings, and a vector store over your sources.
Build
Wire & measure
Build integration & RAG
Implement the middleware and retrieval against your backend, so the feature reads live data through your existing services.
Evaluation
Evaluation sets score answer quality before launch, and regression gates in CI block changes that make it worse.
Ship
Guard & roll out
Guardrails
Input and output checks, PII handling, and a human confirm step on actions that change state or carry risk.
Roll out behind flags + ops
Release to a small group behind a flag, watch accuracy, cost, and latency, then widen once the numbers hold.
AI added to products
already in production
Where we wired model features and retrieval into live systems — a clinical platform, a fintech product, and a document workflow in legaltech.
Medication management software for patients and care teams
Medication management software with two sides, an accessible patient app and a clinical adherence dashboard, joined by bidirectional HL7/FHIR EMR sync.
Crypto trading platform with wallet, card & academy
An integrated crypto trading platform that puts trading, hardware-wallet custody, a debit card, fiat on- and off-ramps, and an education academy under one account.
eIDAS-qualified e-signature platform with KYC & blockchain audit
One platform to sign agreements, verify identities, and collect payments, all eIDAS-qualified with a blockchain audit trail on every signature.
Notes on putting AI
into a live product
Field notes and guides from the engineers wiring model features into systems that are already serving users.
- AI & Machine Learning · 20 min · January 7, 2026
Model Context Protocol (MCP) for Developers
- AI & Machine Learning · 19 min · January 5, 2026
Claude Code Skills: Teaching Your AI Coding Agent Your Stack
- AI & Machine Learning · 18 min · January 2, 2026
The AI Software Engineer: What the Role Actually Is Now
- AI & Machine Learning · 18 min · December 31, 2025
AI Pair Programming: How Senior Engineers Work With AI
- AI & Machine Learning · 18 min · December 29, 2025
AI Coding Tools in 2026: The Landscape, Honestly
AI integration questions
What product teams ask before adding AI to a stack that already ships.
AI integration is adding AI capabilities to software you already run: LLM features, RAG over your data, in-app automation, delivered through APIs and middleware rather than a rebuild. The model and retrieval layer sit alongside your existing services and read from your existing data, so the product gains AI without changing the architecture underneath it.
Yes. We add AI through an API and middleware layer that talks to your current backend, then roll each feature out behind a flag. Nothing in your codebase gets rewritten to ship the first capability. Your team keeps owning the product; we own the AI layer it calls into, and the two stay decoupled.
We index your sources, whether documents, tickets, or records, by chunking them, generating embeddings, and storing them in a vector database. At query time the system retrieves the passages that match and passes them to the model, so answers cite your data instead of the model's training set. Access is scoped to what each user is already allowed to see.
Cost scales with scope. A single LLM feature is a smaller engagement than RAG across several internal systems, and we size the work to the capability you need first. The first feature is a fixed-scope engagement measured in weeks, so you see a working result before committing to a wider rollout.
Two to three weeks for a first feature, shipped behind a flag so you can turn it on for a small group before everyone. That window covers wiring the API layer, building retrieval if the feature needs it, and getting an evaluated version into staging. Wider rollout follows once the numbers from that first slice hold up.
Retrieval respects the permissions your product already enforces, so a user never sees data through the AI that they could not see directly. Tenants stay isolated, and we pick a deployment pattern to match the compliance posture you already operate under: your cloud, a private endpoint, or a model that never trains on your inputs.
Add AI to the product
you already have
Partner with Idealogic to wire LLM features and RAG into your live stack — API-first, evaluated, and rolled out behind flags by senior engineers.