AI features that actually work in production, not just in a demo
We design and build the AI layer of your product: retrieval systems over your own data, LLM integrations that don't hallucinate on your users, and features like search, summarization, and generation that hold up under real traffic.
Built for teams at these stages.
Product teams adding AI features
You have a working product and want to add AI-powered search, summarization, recommendations, or generation without a rewrite.
Companies with a large document or data corpus
You have manuals, contracts, tickets, or internal knowledge that should be searchable and answerable by a system, not buried in folders.
Founders building an AI-native product from scratch
The AI isn't a bolt-on feature, it's the core of the product, and it needs to be architected correctly from day one.
What's inside this service.
- RAG (retrieval-augmented generation) systems that answer questions using your own documents and data, with citations back to source
- LLM integrations wired into your existing product, with proper prompt design, output validation, and fallback handling
- Document intelligence pipelines that extract, classify, and structure information out of PDFs, contracts, and scanned files
- Semantic search that understands meaning and intent, not just keyword matching
- AI-assisted product features: summarization, content generation, recommendations, and auto-tagging
- Evaluation and testing harnesses so you know when a prompt change or model swap makes things better or worse
- Data pipelines that keep your knowledge base and vector store in sync with your source systems
Technical depth across the stack.
RAG and knowledge systems
- Document ingestion and chunking strategy tuned to your content type
- Vector database setup and retrieval tuning (semantic + keyword hybrid search)
- Citation and source-tracing so answers point back to the original document
- Freshness pipelines that re-index when source documents change
LLM integration engineering
- Prompt design and structured output validation (JSON schemas, function calling)
- Model selection and cost/latency tradeoffs across providers
- Guardrails against hallucination: grounding, confidence thresholds, and refusal handling
- Streaming responses and graceful degradation when the model is slow or unavailable
AI-native product features
- Search and discovery powered by embeddings instead of exact-match filters
- Automated summarization of long documents, threads, or transcripts
- Recommendation logic based on usage patterns and content similarity
- Content and draft generation with human review checkpoints built in
Reliability and evaluation
- Test sets and eval scripts to catch regressions before they ship
- Monitoring for output quality, latency, and cost per request
- Human-in-the-loop review flows for anything customer-facing
Everything covered in this engagement.
- Architecture review of where AI adds real value in your product versus where it doesn't
- Data preparation and pipeline setup for whatever documents or sources you're grounding on
- Full engineering build: backend integration, retrieval layer, and UI where needed
- Evaluation suite so you can measure quality before and after changes
- Documentation of prompts, model choices, and system architecture for your team
How an engagement runs.
Discovery
We map your data sources, existing product architecture, and the specific problem the AI feature needs to solve.
Architecture
We decide on retrieval strategy, model choice, and integration points, and flag any data or infrastructure gaps early.
Build
We build the pipeline, integration, and any UI in iterations you can see and test as we go.
Evaluation
We run the feature against real and edge-case inputs, tune retrieval and prompts, and fix failure modes before launch.
Launch
We deploy, monitor early usage, and hand off documentation so your team can maintain and extend it.
Tools and platforms we work with.
Where this service applies.
Support knowledge base with grounded answers
A SaaS company had years of help docs and support tickets nobody could search effectively. We built a RAG system that answers customer questions directly, citing the source article, cutting repetitive support tickets.
Contract review assistant
A legal-adjacent business needed to extract key terms, dates, and obligations from hundreds of contracts. We built a document intelligence pipeline that structures the data and flags anomalies for human review.
In-product semantic search
A marketplace's keyword search missed obvious matches when users phrased things differently. We replaced it with embedding-based search that understands intent, improving match rates without changing the UI.
Pricing for AI Product Development
Starting at Scoped after consultation. See what affects price and how quoting works.
You may also need.
Common questions about AI Product Development.
Ready to talk about ai product development?
Tell us what you are trying to build. We will help you scope it and recommend the right way to get started.