Generative AI

Turn large language models into products people use

We design and engineer production-grade generative AI applications — knowledge assistants, copilots, content engines and AI features inside your existing software.

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The challenge

From impressive demo to reliable product

Getting an LLM to work once is easy. Making it accurate, affordable and safe for thousands of users is engineering. SDLABS handles the hard parts: retrieval pipelines, prompt and tool design, evaluation suites, cost controls and observability.

Whether you are adding an AI feature to a SaaS product or building an internal knowledge assistant, we choose the right model for each task and keep your architecture flexible as the AI landscape evolves.

What we deliver

Generative AI & LLM App Development capabilities

Everything you need to launch and scale generative AI apps — designed, built and supported by SDLABS.

RAG knowledge assistants

Ask-anything assistants over your documents, wikis, tickets and databases with source citations.

AI copilots & features

Smart drafting, summarising, search and recommendations embedded inside your product.

Content generation engines

On-brand product descriptions, ads, emails and reports generated at scale with review workflows.

LLM integration & fine-tuning

OpenAI, Anthropic, Gemini and open-source models integrated, prompted and fine-tuned where it pays off.

Evaluation & cost control

Automated evals, caching and model routing so quality goes up while token costs go down.

Private & secure AI

PII redaction, role-based access and private deployments for sensitive business data.

Use cases

Who it’s for

Generative AI Apps tailored to how your industry works.

SaaS products

Add AI search, summaries and assistants that increase activation and retention.

Internal knowledge

Give teams instant, cited answers from policies, SOPs and past projects.

Legal & compliance

Contract review, clause extraction and document comparison assistants.

Marketing teams

Campaign ideation and content production pipelines that stay on-brand.

Our process

How we deliver generative AI apps

01
Prototype
A working proof of concept on your real data in days, not months.
02
Evaluate
We measure accuracy, latency and cost against a test set you agree on.
03
Engineer
Production APIs, UI, security, monitoring and scalable infrastructure.
04
Evolve
Continuous evals and model upgrades as better LLMs are released.

Tools & technologies

OpenAI
Anthropic Claude
Google Gemini
Llama / open-source LLMs
LangChain / LlamaIndex
pgvector / Pinecone
Python & TypeScript

FAQ

Generative AI Apps FAQs

Straight answers to the questions we hear most.

Which AI model do you use — GPT, Claude or Gemini?

We are model-agnostic. We benchmark models on your actual task and often combine them — for example, a fast model for routing and a stronger model for complex reasoning.

What is RAG and do I need it?

Retrieval-augmented generation lets an AI answer from your own documents instead of general internet knowledge. If accuracy on your business data matters, you need it.