SaaS development and AI integration
We build new SaaS products and add AI to platforms that already have customers on them. Search, drafting, workflow automation and chat, wired into the product you already run.
LangChain, LangGraph, RAG, evaluation
What usually breaks first
Four problems show up in nearly every SaaS engagement we take.
Adding AI without a rewrite
You have a product in production and paying customers on it. Intelligent features go in beside what already works, behind the same auth and the same data model.
Growth that changes the architecture
Query patterns that hold at a thousand users fall over at a hundred thousand. We find the ceiling by measuring it, before your customers do.
Shipping speed without breakage
Faster releases come from tests, reversible migrations and traces you can read at 2am. They do not come from skipping review.
Multi-tenant isolation
Tenant data separation, per-tenant configuration and fair resource limits. Decided once at the schema, not patched into each new feature.
Search that understands the question
Vector search across your own records, so a user finds the right document without guessing your exact wording.
- Vector-based semantic search
- Natural language queries
- Personalised results
- Auto-suggestions
Drafting and summarising
Generation, summarisation and rewriting inside the screens your users already work in, not in a separate AI tab.
- Draft generation
- Content summarisation
- Tone adjustment
- Template expansion
Automated workflows
Routine triage handled by the system: categorising incoming work, assigning it, and flagging what looks wrong.
- Smart categorisation
- Auto-assignment
- Anomaly detection
- Predictive actions
Conversational interfaces
Chat grounded in your product data, so answers cite the record they came from instead of inventing one.
- RAG-powered chat
- Context-aware responses
- Multi-turn conversations
- Integration with product data
AI and LLM work
Most of the difficulty in an AI feature is not the model. It is retrieval quality, cost per request, and knowing whether the last change made answers better or worse.
LangChain and LangGraph
Production implementations for document processing, retrieval and agentic workflows.
Vector databases
Pinecone, Weaviate, Qdrant and pgvector, chosen against your read volume.
LLM orchestration
Multi-model routing, fallbacks and cost control across OpenAI, Anthropic and others.
RAG systems
Retrieval that grounds every answer in your product's own data.
Fine-tuning support
Custom fine-tuning for domain tasks, when a general model keeps falling short.
Evaluation and monitoring
Eval sets and production traces, so a prompt or model change is measured before it ships.
Three ways we work with SaaS teams
Pick the one that fits your team. Moving between them mid-engagement is normal.
Adding RAG to a knowledge base
We put retrieval-backed search and question answering into a SaaS knowledge management platform that was already live. The core product kept shipping while we worked.
- Retrieval runs against the existing document store. Nothing was migrated.
- Every answer links to the source document, so a wrong answer is traceable.
- Retrieval falls back to keyword search when confidence is low.
- The evaluation set was built from real support questions before launch.
What we build on
| Layer | Tools |
|---|---|
| AI and LLM | LangChain, LangGraph, OpenAI, Anthropic, Pinecone, Weaviate, Qdrant |
| Backend | Python, Node.js, Go, FastAPI, Django, Express |
| Frontend | React, Next.js, TypeScript, Tailwind CSS |
| Infrastructure | GCP, AWS, Kubernetes, Terraform, PostgreSQL, Redis |
Tell us what your product cannot do yet.
A 30 minute call about the feature you keep postponing. We will say whether AI is the right tool for it, and if it is not, we will say that instead.
Book a call- First reply
- same working day
- Built in
- Lahore, Pakistan