Tinkerberry Labs
How it works

Your first workflow runs in weeks.

No six-month discovery. The first workflow runs on your real data inside a month, and you decide what happens next from what it saved.

Talk to an engineer
The engagement

Four phases, priced up front.

01Week 1

Audit

We sit with your team and follow the work: which tasks repeat, how long they take, what they cost, and which ones a machine can do. You keep the ranked list and its payback periods whether or not you hire us.

02Weeks 2-4

Pilot

We build the highest-payback workflow first, on your real data, in your real systems. It runs alongside your team so you can compare its output against theirs before anyone depends on it.

03Weeks 4-8

Deploy

We harden the integrations, set the guardrails, wire in approvals and train the team. The dashboard then shows what the system handled, what it escalated and what it saved.

04Ongoing

Run and expand

We monitor, tune and keep pace with your process changes and with the models. Once the first workflow pays for itself, we work down the roadmap.

Guardrails

What keeps an AI system inside its lane.

All six ship with every build. We do not sell them back to you later.

Scoped tools

An agent can only call the systems and actions we explicitly grant it. There is no general internet access unless the task requires it and you approve it.

Confidence thresholds

Anything the system is unsure about goes to a person with the evidence attached, rather than being guessed at.

Human approval gates

Money movements, customer commitments, contract terms and anything else consequential need a person to confirm.

Full logging

Every input, decision and action is recorded, so you can audit any outcome months later.

Evaluation suites

We build test sets from your real cases so quality is measured on every change, not assumed.

Kill switches

Every workflow can be paused instantly without taking the rest of your operation down with it.

Questions

Everything else people ask.

It stays in your systems wherever possible. We use zero-retention model endpoints so nothing is used for training, deploy inside your own cloud when required, log every access, and document the full data flow for your compliance team.

We design for that from the start. Agents work within a scoped set of tools, confidence thresholds route uncertain cases to a person, consequential actions need approval, and everything is logged. We also build evaluation suites so quality is measured, not assumed.

That is the normal starting point. Part of the audit is writing down what happens today, which is often the first time anyone has. Messy is workable; unknown is not, and we fix unknown first.

Yes. We can deliver end to end, embed alongside your team, or design the architecture and hand it over with documentation and tests so your engineers own it.

Whatever fits the job and the budget: frontier models from Anthropic, OpenAI and Google, open-weight models where cost or residency demands it, plus the workflow and data tooling you already run. We route by task complexity to keep unit costs sane.