Embedded AI Engineer
One senior AI engineer joins your team and works inside your existing sprints, tools, and codebase. Good fit when you already have a roadmap and need extra hands.
AI Development Pricing
Know your price before the work begins
Custom AI development for AI-powered products, ML models, chatbots, and agentic systems. You'll know the delivery model, scope, and price before anything starts.
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 product team needs to ship an AI feature that's been sitting on the roadmap. An embedded AI engineer joins for a defined stretch, works inside your existing sprints and codebase, and ships the feature alongside your team instead of in a silo.
Common patterns we build
A sample of the technical work that shows up under this delivery model, not an exhaustive list.
AI feature integration
Wiring AI capability into your existing product surfaces and user flows, rather than bolting on a separate tool your users have to leave the product to use.
Model and vendor selection
Choosing the right foundation model, provider, and hosting setup for your latency, cost, and data requirements, instead of defaulting to whatever's newest.
Data and API integration
Connecting AI components to your existing databases, internal APIs, and third-party systems so the feature works with real data, not a demo dataset.
Evaluation before shipping
Testing against a structured set of real inputs and edge cases before launch, rather than shipping on spot-checked outputs.
Length
Set after scoping
Team
Sized to the work, from one engineer to a full pod
Output
A shipped feature, prototype, or system milestone
Engagement
Embedded, fractional, project-based, or managed pod
Pricing
Scoped after a working session
SOW once scoped
Confirms your exact workflow
Fractional AI Technical Talent
Senior AI talent for a set number of hours or days each week. Good fit when the need is real but doesn't add up to a full-time seat yet.
Managed AI Development Pod
A small team of engineers, with product and QA support as needed, owns a defined build from start to finish. Good fit when the scope is bigger than one person can carry.
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Project-Based AI Build
A defined AI feature or system, scoped up front and delivered against clear milestones. Good fit when you already know what you want built.
Because scope varies so much between products, codebases, and roadmaps, we don't publish a rate card for this work. We scope it with you first, and you'll know the exact price before any work begins, not after.
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 just add an engineer to our existing team?
Yes, that's the embedded engineer model. They work inside your sprints, tools, and codebase alongside your team rather than as a separate workstream.
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Ready to ship your next AI feature?
Tell us what's on your roadmap and we'll recommend the right way to staff it.