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.
Agentic AI Development Pricing
Know your price before the work begins
Build AI features, automations, internal tools, and product capabilities with senior engineers. 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
An operations team is buried in a repetitive multi-step process, like triaging support tickets or reconciling data across tools. We build an agent that can take the actual actions, not just suggest them, with the guardrails and human checkpoints your team is comfortable with.
Common patterns we build
A sample of the technical work that shows up under this delivery model, not an exhaustive list.
Tool-calling and action design
Defining exactly which actions the agent can take and how it calls into your existing systems, so it does real work instead of just describing what it would do.
Guardrails and human checkpoints
Deciding where the agent can act on its own and where it should hand off to a person, matched to how much risk that action actually carries.
Multi-step orchestration
Chaining tools and steps together reliably, including what happens when one step fails partway through a longer process.
Audit logging and traceability
Tracking what the agent did and why, so any action it took can be reviewed and explained after the fact.
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.
How do you handle guardrails on an agent that takes real actions?
We define the checkpoints together during scoping, including where the agent should act on its own and where it should hand off to a person before proceeding.
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Ready to put an agent to work?
Tell us which process is eating your team's time and we'll recommend the right way to automate it.