In-app copilots that activate and retain
Assistants embedded in your product that help the user do more in fewer steps, directly impacting activation, retention and account expansion.
Applied AI · SaaS & Startups
Applied AI for SaaS & Startups
We turn AI into real features in your SaaS: in-app copilots, RAG over each customer's data and workflow automation that increase retention and set your product apart.
THE CHALLENGE
The challenges of SaaS & Startups
SaaS startups need to reach the market fast without mortgaging the future with technical debt. The software must iterate quickly, scale in multi-tenant and sustain a solid technical story for investors.
- Speed to market without technical debt
- Scaling the architecture (multi-tenant)
- Keeping cloud costs under control
- Iterating fast with data
- A solid base to raise investment
How we help you
For a SaaS, AI isn't a marketing checkbox: it's a feature that has to retain, activate and differentiate. What matters isn't dropping in a chatbot, but embedding copilots and RAG where the user already works, over their own data, with cost and latency control so the margin doesn't evaporate. We apply artificial intelligence to the workflows that move your product metrics —activation, retention, expansion— and take it from prototype to production with business judgment, evaluation and guardrails.
RAG over each customer's data
Answers and content grounded in each tenant's data, with per-customer isolation and guardrails so information doesn't leak between accounts.
Automation of internal and product workflows
Agents that chain repetitive tasks —classify, enrich, draft, route— inside your product and your operations, freeing up time and reducing errors.
From prototype to production with cost control
We pick models per use case, control cost and latency and add evaluation, so the AI feature is sustainable on margin and reliable at multi-tenant scale.
WHAT YOU GAIN
Applied AI, results-driven
- Use cases prioritised by business impact
- Integrated with your data and your product, not an isolated chatbot
- Model cost and latency under control
- Evaluation and guardrails for reliable answers
- From prototype to production with judgement
FAQ
Frequently asked questions
How do you isolate each customer's data in a multi-tenant RAG?
We design the RAG with per-tenant isolation in both storage and retrieval, plus guardrails that stop one account from accessing another's data. Data separation is an architecture requirement, not an extra.
Do AI features really increase retention?
That's the criterion for building them. We prioritise use cases by their impact on your product metrics —activation, retention, expansion— and measure the real effect. If a feature doesn't move the needle, we don't take it to production.
How do you control model cost at scale?
We pick the right model per use case, cache, bound context and monitor cost and latency per feature and per customer. That keeps the margin healthy as you grow in users and usage.
Can you add AI to a product that's already live?
Yes. We come in over your existing product, identify where AI adds value and integrate it incrementally, with evaluation and guardrails, without rewriting your platform or slowing your roadmap.
Will AI actually work for my case or is it just a demo?
We start from the business problem, not the technology. If a use case doesn't deliver measurable value, we'll tell you. We only take to production what justifies it.
Do you use your own models or third-party ones?
Both, as appropriate: commercial models via API to move fast, and open or fine-tuned models when cost, privacy or latency demand it.
Shall we build something that scales, together?
Tell us about your project. We'll get back to you with a concrete plan and a senior team — not a generic quote.