Know your price before the work begins
LLM integrations, RAG systems, AI chatbots, and generative applications, built and evaluated for production. You'll know the delivery model, scope, and price before anything starts.
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Scoped with you
Built for funded startups, growth-stage technology companies, and mid-market product companies with a real AI product roadmap and not enough engineering capacity to ship it alone. Not currently built for enterprise product programs requiring extensive vendor certification or enterprise-wide rollout governance.
Typically brought in by CTOs, VPs of Engineering, Heads of Product, technical founders, and engineering managers.
What this often looks like
A company wants a chatbot or internal assistant that actually knows their content, not a generic wrapper around a model. We build the RAG pipeline, connect it to your knowledge base or documents, and evaluate it against real questions before it goes live.
Common patterns we build
A sample of the technical work that shows up under this delivery model, not an exhaustive list.
RAG pipeline architecture
Chunking, embedding, and retrieval tuned to your actual content and how people ask questions, not a generic wrapper around a model.
Retrieval evaluation
Testing retrieval quality against real questions before ever tuning the prompt, since a chatbot pulling the wrong context can't be fixed with better wording.
Response evaluation harnesses
Structured testing against edge cases, ambiguous questions, and failure modes, rather than spot-checking a handful of happy-path examples.
Grounding and fallback design
Defining what the system says when it doesn't know the answer, so it stays grounded in your source content instead of guessing.
Embedded AI Engineer
Fractional AI Technical Talent
Managed AI Development Pod
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Project-Based AI Build
Frequently asked questions
How is pricing determined if there's no rate card?
Scope varies too much by product, codebase, and roadmap to price in advance. We scope pricing together first, so you'll know the exact number before any work begins, not partway through.
What's the difference between the delivery models?
An embedded engineer fits when you already have a roadmap and need extra hands. Fractional talent fits when the need is real but not full-time. A managed pod fits when the scope is bigger than one person can carry. A project-based build fits when you know exactly what you want and want a fixed endpoint.
Who leads the work on Tecla's side?
We match senior AI engineers to your stack, roadmap, and the specific delivery model you choose, whether that's one embedded engineer or a full managed pod.
Do you work with early-stage startups, or only larger companies?
Funded startups, growth-stage technology companies, and mid-market product companies are the strongest fit, provided there's clear product ownership and someone who can make decisions quickly. This isn't currently built for large enterprise product programs.
Can you build a RAG-powered chatbot or assistant for us?
Yes, that's one of the more common projects under this delivery model. We connect it to your actual content, and we evaluate it against real questions rather than shipping it on vibes.
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