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SaaS AI Integration
Your product works. Users are happy with it. But they're starting to ask why search still requires exact keywords, why documents still need manual review, and why there's no assistant that knows their data. You don't want to rebuild from scratch or spend six months on a new platform to answer those questions.
We audit your existing product, identify the 2–4 highest-impact AI insertion points, and build them against your current stack. Most integrations ship in 4–8 weeks. Your existing database, auth layer, and API contracts stay in place.
Tell us what you're building.
These are the six categories that come up most often. Not every product needs all of them, the audit determines which two or three will move the needle for your users.
Replace keyword search with vector search over your content. Users find what they mean, not just what they typed.
Context-aware assistant that knows your product's data model. It answers questions and suggests actions based on what the user is actually doing.
Extract, classify, and summarise uploaded files automatically. PDFs, Word documents, and spreadsheets processed at ingestion, not on demand.
Categorise records at creation time using your taxonomy. No manual tagging, consistent output, and a confidence score on every label.
Surface the right action for each user at each step, the next form to fill, the record to update, the contact to reach out to.
Users query your data in plain English. "Show me deals closed last quarter over $50k" works without custom filter UI.
We don't rip out your database or rewrite your backend. The integration pattern is additive: a vector store sits alongside your primary DB, a thin AI service layer calls your existing APIs, and every feature ships behind a feature flag so you can roll back instantly if something behaves unexpectedly.
Before we write a line of code, we spend time with your codebase. We read the data model, map the existing API surface, and identify where the AI layer plugs in with the least disruption. The result is an integration plan your engineers can review and push back on before we start building.
Each feature gets its own eval dataset, a set of representative inputs with expected outputs, so you have a concrete way to measure whether the feature is working and catch regressions after model updates.
Adding a model call to a single-user tool is one thing. Adding it to a product with many paying customers is another, because four multi-tenant problems show up the moment real accounts touch it.
Isolation has to hold through the AI path. The fastest way to lose an enterprise customer is for one tenant's data to appear in another's results. We enforce tenant boundaries at the retrieval and prompt layer, not just the database, so context assembled for one customer can never leak into another's request, and we test that boundary rather than assume it.
Cost has to be attributable per tenant. A single AI invoice tells you nothing about which accounts drive it. We meter token cost per tenant and per feature so you can see the heavy users, the unprofitable plans, and the feature patterns that move the bill. Without that, an AI feature becomes an unallocated expense your finance team eventually questions.
Your SLA should not inherit a vendor's bad day. The model API will time out or rate-limit during your busiest hour. We build the feature to degrade gracefully, a cached result, a clear message, or a fallback path, so a provider incident affects one feature rather than taking your product down and breaching the uptime you promised customers.
Quality is a distribution, not an average. A feature that works for most accounts can quietly fail for the customer whose data is scanned faxes or in another language. Those accounts rarely complain; they just stop using the feature and show up as churn two quarters later. We slice quality by tenant cohort so the bottom of the distribution is visible while it is still fixable.
We turn away projects that won't produce a good result. It saves time on both sides.
Greenfield AI products
New products that should be designed AI-first from day one belong in a different engagement. This service is specifically for adding AI to a working product.
Products that need a rebuild first
If your core product has significant technical debt that blocks clean AI integration, we'll tell you that on the first call rather than bill you for work that won't hold.
Chatbots trained on a marketing site only
If the goal is a support widget that reads your docs pages, that's a one-day configuration job, not an integration project. We work on products where the AI needs to read and act on real application data.
Describe which part of your product you want to improve. We'll reply within one business day with a rough scope and price range, no commitment required.