Bring AI into the product
you already have
You do not need a rewrite, a new platform or an AI team. We add a thin, well-instrumented layer next to your existing stack, and ship one useful feature at a time, with the evidence that it works.
on your real data
into live products
by an evaluation set
to get started
What we actually plug in
Six patterns cover almost every request we get. Each one is a feature in your product, not a demo in a sandbox.
In-app assistants
A copilot that knows your product, the customer’s account and the screen they are on, answering inside the interface they already use.
Search that understands
People ask in their own words and get the right record. Semantic retrieval across your database, documents and tickets, with the source cited.
Document understanding
Contracts, invoices, forms and PDFs turned into structured fields your system can act on, each with a confidence score attached.
Classify and route
Tickets, leads and messages sorted, tagged and sent to the right queue as they arrive. Usually the quickest place to see a return.
Agentic workflows
Multi-step jobs that call your own APIs, check their own output and pause for a person at the points where a mistake would be expensive.
Voice and transcription
Calls and meetings transcribed, summarised and written into the record, so nobody has to type up the notes afterwards.
How it plugs in
Your product keeps its architecture. We add one service in the middle: the part that makes AI safe to put in front of customers.
The product you have
- Existing app and database
- Your auth and permissions
- Your release process
- No framework change
The integration layer
- Model gateway and routing
- Retrieval over your own data
- Guardrails and output schemas
- Evaluation set on every change
- Cost, latency and trace logging
Models and storage
- Frontier or self-hosted models
- Vector store for retrieval
- Caching layer
- Swappable, not hard-wired
Because the gateway sits in the middle, changing model or moving one in-house is a configuration change rather than a project.
Five steps, no leap of faith
Each step ends in something you can look at and judge for yourself.
Find the case
Two days on your product, your data and your support queue to pick the feature with real return and a small blast radius. If we cannot find one worth building, we say so.
Prototype
A working version on your actual data in two to three weeks. Enough for you to judge it honestly, and deliberately short of production quality.
Integrate
The layer goes in beside your stack, behind your auth and inside your release process. Your framework, deploy pipeline and on-call rotation stay as they are.
Evaluate
A scored test set built from your own examples and edge cases. Every prompt or model change runs against it, and nothing reaches production until it clears the bar you set.
Operate
Cost per request, latency and answer quality on one dashboard, with someone accountable when a number moves the wrong way.
Built by a team that ships
The AI layer is new. The engineering underneath it is not. These are products we designed, built and still maintain.
Data collection at scale
A digital market study and data-collection platform built for fast, reliable research at scale.
Product development · Frontend
Digital marketing
One content workflow
Planable bundles social media collaboration tools into one seamless content workflow for marketing teams.
Web development · Performance
Social platform
Built for real connection
OPEN social merges the benefits of social networking with real, meaningful human connection.
Mobile · ProductModel-agnostic by design
Each layer is chosen per project, and every choice stays reversible.
Claude, GPT, Gemini, Llama or Mistral
Chosen per task on quality, latency and cost. Most products end up using two or three: a strong model for the hard calls, a small fast one for the volume.
pgvector, Qdrant, or the index you already have
Usually whatever your database already runs. A separate vector store is a decision we take only when the volume or the query pattern calls for one.
Model Context Protocol, LangGraph, or plain application code
Plain code until the workflow genuinely needs a graph. A framework earns its place when steps branch, retry and run in parallel.
Whisper, or a provider’s realtime API
Decided by accuracy on your accents and your vocabulary. We benchmark on your own recordings first, because published word-error rates rarely survive contact with real calls.
The provider API, your own VPC, or fully on-prem
Set by your data policy. If the data cannot leave your infrastructure we run an open model inside it, and tell you what that costs in capability.
“Inventiff. has made a lot of good suggestions along the way and has become part of our team.”
Axel Heinz · CPO, DGfB mbH Verified review on ClutchGot an idea? Let’s make it real.
Tell us the short version
This could be the first step towards a new and successful collaboration. A one-line idea and a finished spec are both fine — tell us the problem, the deadline you’re working to and what’s in your way.
Frequently asked questions
No. We add a service alongside your existing stack, connect it to the data and screens that matter, and ship feature by feature. Your architecture, framework and release process stay as they are.
Whichever fits the job and your constraints. Claude, GPT, Gemini or an open model you host yourself. We put a gateway in front, so changing model is a configuration change. Most products end up using two or three for different tasks.
Three things, in order: ground answers in your own data so the model has something real to work from, constrain the output shape so it cannot drift, and run an evaluation set on every prompt change so a regression is caught before release.
Not on the enterprise API tiers we deploy on, and we configure zero-retention where the provider offers it. If your policy requires the data to stay inside your infrastructure, we run an open model there instead.
It is measurable from day one. We set per-feature token budgets, cache aggressively and route cheap work to small models. You get a cost-per-request figure alongside the latency figure before anything reaches production.
A working prototype on your real data in two to three weeks. A first feature in production, with evaluations and monitoring behind it, usually inside six to eight weeks.