Copilots & Chat Interfaces
Conversational assistants embedded in your application or internal tools, grounded in your own data and workflows.
AI Solutions · Generative AI Integration
Generative AI that works
inside your business.
We embed large language models and generative AI into your existing products and workflows, from copilots and chat interfaces to document and content automation, built with careful evaluation and guardrails.
Overview
From impressive demo to dependable product.
Getting a language model to produce an impressive answer takes an afternoon. Making it accurate, safe, affordable and maintainable inside a real product takes engineering. That gap is where most generative AI projects stall.
Tekleads engineers close it. We work out where generative AI creates real value in your workflows, and where it does not, then design and build the integration: prompts, retrieval, evaluation, guardrails and monitoring included. The result is a feature your users can trust and your team can maintain.
What's included
Practical generative AI, end to end.
Retrieval-Augmented Generation
RAG pipelines that connect models to your documents and knowledge bases, so answers are current, sourced and specific to your business.
Document & Content Automation
Summarization, extraction, classification and drafting that take repetitive language work off your team's plate.
Prompt Design & Evaluation
Systematic prompt engineering, with test sets and metrics that show whether a change actually improves results.
Guardrails & Safety
Input and output controls, access boundaries and human-in-the-loop review where the stakes require it.
Cost & Latency Optimization
Model selection, caching and architecture choices that keep response times fast and usage costs predictable.
Where it fits
Use cases we build for.
Customer support assistants
Internal knowledge search
Contract & document analysis
Report & content drafting
Data extraction from unstructured text
Developer productivity tools
Sales & marketing enablement
Why Tekleads
Built to be trusted in production.
We treat generative AI as software engineering, with testing, monitoring and accountability, not as a one-off experiment.
Value before technology
We start from the business problem and are willing to tell you when generative AI is not the right tool.
Evaluation, not guesswork
Measurable quality criteria and test sets, so improvements are demonstrated rather than assumed.
Model-agnostic
We recommend the model and architecture that suit your data, budget and risk profile, not a preferred vendor.
Technology
The generative AI toolkit.
LLMs & Frameworks
Data & Retrieval
Cloud AI
How we work
How we take generative AI to production.
Assess & scope
We evaluate your data, use case and constraints to find where generative AI creates real value.
Prototype
A working proof of concept on your data, fast enough to test the idea before you commit real budget.
Harden & validate
Evaluation, testing and guardrails, so the solution is ready to meet real users.
Deploy & operate
Integration into your stack, plus monitoring and cost management once it is live.
FAQs
Common questions
Will our data be used to train public models?
We design integrations with your data-handling requirements in mind, selecting providers, deployment options and configurations that keep your data under the terms you require. We review this with you at the scoping stage.
How do you deal with hallucinations?
By grounding models in your own data through retrieval, constraining outputs, evaluating against test sets and adding human review where accuracy is critical. Hallucination risk can be reduced significantly, but designing around it is essential.
Do we need a large amount of data to start?
Not necessarily. Many generative AI use cases rely on pre-trained models plus your documents and knowledge bases, rather than large labeled datasets.
Can you integrate with our existing applications?
Yes. Integration into existing products, internal tools and workflows is the core of what we do, using APIs and the technology stack you already have.
Have a generative AI use case in mind?