AI inside your product.
We add AI, machine learning and LLM features to products already in production, without stopping what works. From a single model to a full agent, evaluated, monitored and rolled out step by step.
Tell us about your product- For
- Product teams with software in production
- Starts with
- An audit of your product and data
- Then
- The feature in production, rolled out in stages
- Examples
- US contact center call scoring, fintech risk agent
Ship it without
stopping it.
The model is not the hard part: fitting it into a system that already has users is.
Integrating AI into an existing product means a model takes over part of the work (classifying, scoring, summarising, extracting data, answering) inside your architecture, without changing what your users already rely on. It can be a commercial LLM, a model trained on your data, or both. What decides whether it works is everything around it: how it receives data, what happens when it is slow or fails, and how you measure whether it is right.
- AuditYour product, your data, and where AI adds real value and where it does not.
- ArchitectureAPIs, queues or event streams, so the model never blocks the main flow.
- ModelAn LLM, a custom model or both, chosen on quality, cost and privacy.
- EvaluationReal cases with expected outputs, run on every change. This is where most integrations fail.
- RolloutBehind feature flags, to a small group first, with a way back.
- MonitoringQuality, cost and latency of every request, in production.
- REST APIs
- Queues
- AWS Kinesis
- Feature flags
- LLMs
- Custom ML
- Langfuse
- AWS Bedrock
- Vertex AI
- Azure OpenAI
The terms, in one sentence
- Feature flag
- A switch in the code that turns the new feature on only for the users you choose, and off again without a redeploy.
- Queue
- Requests to the model are processed separately, so a slow response does not hold up the rest of the application.
- Event stream
- Data processed as soon as it arrives (for example with AWS Kinesis), useful when AI has to react in real time.
- Evals
- Sets of real cases with expected answers, to know whether a model or prompt change made results better or worse.
- Fallback
- What the product does when the model fails or is too slow: a rule, a default answer or a person.
- Observability
- A record of every model call with its input, output, cost and latency.
What you get.
- Audit and planWhere AI adds value in your product, what it costs and what it risks, written down.
- IntegrationThe feature in your codebase, with its APIs, its interface and its feature flags.
- EvaluationTest suites built from real cases that run on every change.
- Measured cost and latencyWhat each request costs and how long it takes, before you switch it on for everyone.
- ComplianceRisk classification under the EU AI Act and data handling in line with GDPR.
- You own the systemCode, infrastructure and documentation, in your repository and your cloud account from day one.
What people ask us.
How do you add AI to an existing SaaS product?
We start with an audit of the product and its data to decide where AI adds value. Then we integrate it as one more service, through an API, a queue or an event stream, behind feature flags, and switch it on for a small group of users first.
Can adding AI slow down or break our product?
Not if it is integrated properly. The model runs outside the main flow, with timeouts and a fallback when it fails or is slow. And because it sits behind feature flags, it can be switched off without a redeploy.
What have you integrated AI into before?
For example, a speech and scoring pipeline that transcribes and scores 100% of calls at a US contact center in real time, and a risk-analysis agent inside a European fintech’s banking platform, traceable back to every source document.
Should we use an LLM API or a custom model?
It depends on the task. To understand or generate text, almost always an LLM on AWS, Google Cloud or Azure. For narrow, repeated decisions (scoring, classifying, ranking), a model trained on your data is usually cheaper, faster and easier to explain. We often combine both.
How do you handle the EU AI Act and GDPR?
We classify the system’s risk level, document how it works, log every decision and keep human oversight where it is needed. Data stays in the region you choose and is never used to train third-party models.
Do we own the system?
Yes. The code, infrastructure and documentation live in your repository and your cloud account from the first sprint. If you want, we keep running and evolving it with you.