AI Product Engineering for SaaS & Software Companies

We add AI capability to software you already ship — search, copilots, document intelligence, and workflow automation — with tenant isolation, evaluation, and cost controls designed in rather than retrofitted.

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AI Search
In-Product Copilots
Document Intelligence
Tenant Isolation
Cost Controls
4–8w
Typical AI feature delivery
From scoping to production deployment without disrupting your existing product.
49k+
Open-source RAG tool downloads
Our open-source RAG tooling is used by engineering teams building production AI pipelines.
3+
Years of production AI delivery
We've been shipping reliable AI systems into production since day one, not just building demos.
What We Build

AI features built for SaaS products

Every feature integrates with your existing product, data, and infrastructure , no full rebuild required.

AI Search Over Customer Data

Search that understands what a user meant, not just what they typed — scoped to that tenant's own data, so results never cross an account boundary.

Semantic SearchTenant ScopingRelevance TuningHybrid Retrieval

In-Product Copilots

An assistant inside your product that answers questions about a customer's own data and completes tasks through your existing APIs, with permissions enforced.

In-Product AssistantAPI ActionsUser ContextPermission-Aware

Document Intelligence Features

Let your customers upload documents and get structured data back — extraction, classification, and Q&A over the files they already store in your product.

ExtractionClassificationDocument Q&AConfidence Scoring

Workflow Automation Inside Your Product

Multi-step actions your customers currently do by hand, executed on their behalf — with approval steps where the action is consequential.

Multi-Step ActionsApproval GatesAudit TrailTool Execution

Summaries and Generated Content

Drafting, summarisation, and reporting features that turn the data already in your product into something a user would otherwise write themselves.

SummarisationDraftingReport GenerationTemplating

Model Integration and Cost Control

The layer underneath all of it: model routing, caching, streaming, per-tenant usage metering, and the evaluation set that lets you change any of it safely.

Model RoutingCachingUsage MeteringEvaluation
Why Us

AI features that ship in weeks, not months

Most AI vendors want to rebuild your product. We integrate AI into what you already have , connecting to your existing database, APIs, and user workflows without disruption.

Underneath, we use LangChain and LangGraph for orchestration, Qdrant or pgvector for retrieval, OpenAI or Claude behind a routing layer so the choice stays reversible, and FastAPI for low-latency endpoints — integrated with the Next.js or React frontend and Postgres or Supabase backend you already run.

No Full Rebuild

AI features added to your existing product via APIs and integrations.

Your Data, Your AI

RAG systems trained on your product data , not generic LLM responses.

Fast Delivery

Most AI features shipped in 4–8 weeks with bi-weekly demos.

Scales With You

Architecture designed to handle growth from 100 to 100,000 users.

Core AI Services

Custom Software DevelopmentOperational platforms, integrations, legacy modernisation
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AI Agents & Workflow AutomationMulti-step workflows, approvals, system integration
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Intelligent Document ProcessingClassify, extract, validate and route documents
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AI Product & Application DevelopmentNew AI products, AI features, prototype to production
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FAQ

Common questions

By treating it as a change to a live system rather than a greenfield build. That means understanding the existing codebase first, adding an evaluation set for the new behaviour before shipping it, and putting the feature behind a flag so it can be rolled out to a subset of tenants and rolled back without a deploy. The AI work is usually the smaller half of the effort.
Tenant isolation is enforced in the retrieval and tool layer, not in the prompt — the candidate set is filtered by tenant before anything reaches the model, and tool calls execute under scoped credentials. Prompt instructions are never the boundary, because untrusted content in a customer's own documents can override them. This is the single thing most likely to surface in an enterprise security review.
It depends on request volume and how much context each request needs, and it is worth modelling before you build rather than discovering on a bill. We design for it directly: routing simple work to smaller models, caching what repeats, keeping context tight, and metering usage per tenant so you can see which customers and features drive cost — and price accordingly.
No. Provider access sits behind a routing layer, so models can be swapped per feature or replaced entirely when pricing, capability, or availability changes, and different features in the same product often use different providers. Fallback logic keeps the feature available when one provider degrades. The evaluation set makes any switch a measurable decision rather than a leap of faith.
With an evaluation set of real cases, versioned alongside your code and run in CI before every change. Without one, teams end up afraid to touch anything, because quality shifts are only detectable through customer complaints. We build it early and hand it over, since it is what makes ongoing iteration possible after we are gone.
A well-defined feature such as AI search or a summarisation flow is typically 4 to 8 weeks including the evaluation harness. Copilots that take actions through your APIs, and anything requiring a formal security review, sit at the longer end. We deliver working software every two weeks rather than a single launch.
Yes, and that is the usual pattern — the value is in a customer's own data, not in a generic model. We connect to your existing Postgres, Supabase, or custom backend through a typed tool layer with tenant scoping enforced, and add a retrieval layer over your data where semantic search is needed rather than replacing your database.
Get Started

Is Your AI Actually Working in Production?

Most AI demos fail when real users and real documents arrive. Book a free 30-minute discovery call. We'll review your RAG system or AI agent and tell you exactly what's broken.

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