Most teams don't need a new AI product — they need one or two AI tools inside the product they already ship.

This engagement is for that middle ground: the product is real, the users are known, and the AI feature has to fit the current auth, permissions, observability, release process, and support model.

Where we fit

A search box that actually understands user intent.
A workflow assistant that drafts the first 80% of an internal task.
A classifier that triages tickets before they reach a human.
A review queue where AI suggests the next action but a human owns approval.
A support or sales workflow that needs retrieval, summarization, and auditability in one path.
An internal agent that calls approved tools, updates structured records, and stops for review before irreversible actions.
A knowledge workbench that combines search, citations, drafting, and approval history inside the product your team already uses.

What makes it agentic

Agentic does not mean "let the model do whatever it wants." It means the system can take bounded action through tools: read the right context, decide the next step, call an approved function, verify the result, and either continue or hand off.

For existing products, that usually means:

Tool contracts for the actions the AI can take.
Permission checks that match your existing roles.
State machines for review, escalation, retries, and failure.
Evals for quality, grounding, refusal, cost, and tool correctness.
Observability that shows what the model saw, what it did, and why the workflow stopped.

How we build it

Choose one workflow. We pick the smallest workflow where an AI feature can save time without taking ownership away from the user.
Write the eval. We collect real examples, expected outputs, refusal cases, and thresholds before we tune prompts or choose models.
Design the tool boundary. We define what the AI can read, what it can write, which tools it can call, and where a human must approve.
Ship through your stack. The feature uses your auth, permissions, CI, analytics, logging, and support process. We do not build a parallel product unless the constraints require it.
Put a user in the loop. A real user touches the feature by week 2. Their edits and rejections become part of the eval.
Leave the operating kit. Your team gets the eval harness, prompts, observability hooks, model-cost notes, and runbook.

Constraints

We respect the rest of your stack. We don't ask you to rip out auth, observability, or your CI pipeline. AI features ship through the same release process as everything else.

What we won't do

Add AI where the workflow has no measurable success criterion.
Ship a generated output directly to customers when the domain needs human approval.
Hide a brittle prompt behind a polished UI and call it production.
Take over product ownership from the team that will operate the feature.

The arithmetic

Most smart-app work lands inside the Build range: $40–80k for a bounded feature, $80–160k when the workflow touches multiple systems or regulated review. Discovery-only starts at $8k when the first question is whether the feature should exist at all.