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/ML Development Pricing
Know your price before the work begins
Machine learning models, computer vision, and recommendation engines, engineered on your data. 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 company has usage or transaction data sitting mostly unused. We build the model, whether that's a recommendation engine, a computer vision system, or a predictive model, trained on your own data and evaluated against a metric that matters to your business, not just accuracy on a benchmark.
Common patterns we build
A sample of the technical work that shows up under this delivery model, not an exhaustive list.
Feature engineering from raw data
Turning your existing usage, transaction, or behavioral data into inputs a model can actually learn from, usually the part that takes longer than the modeling itself.
Model selection matched to the problem
Choosing between recommendation, classification, computer vision, or forecasting approaches based on what you're actually trying to predict, not a one-size-fits-all default.
Evaluation against a business metric
Measuring against what actually matters, like conversion lift or reduction in manual review, rather than accuracy on a benchmark no one in your business looks at.
Drift monitoring after launch
Watching for when the model's performance degrades as real-world data shifts, so it gets caught before it quietly gets worse.
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
SOW once scoped
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.
Do you need clean data before we start?
No. Most companies come to us with messy or partial data. Assessing what you actually have and what it can support is part of the scoping conversation, not a prerequisite for it.
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Ready to put your data to work?
Tell us what data you have and what you're trying to predict or recommend, and we'll take it from there.