Lab / Tools
m00labs runs as a small engineering lab. That means the patterns, evaluation and tooling built for one engagement make the next one faster and more reliable — and every build stays model-agnostic and stack-honest.
A workflow, stage by stage
Illustrative concept- IngestWork arrives the way it already does — no new portal for anyone to adopt.
- ExtractFields and text are pulled out of the source, with the original kept for reference.
- ValidateDeterministic rules run first: shape, required fields, duplicates.
- ReviewA person decides the edges; the model is never the last word.
- PublishOnly confirmed output is written into the systems where the work is used.
- Synthetic sampleEvery figure in the demonstration uses synthetic data. It is not a client workflow and it reports no real results.
The lab, not the assembly line
A lab builds each system for the problem in front of it, then keeps what it learns. Automation and integration work is not delivered from a template catalogue: the workflow, data and failure modes of your operation decide the design. What the lab reuses is the hard-won layer underneath — how to call models reliably, how to test outputs that are probabilistic, how to monitor a workflow that touches three systems, how to document a handover so a new operator can take over.
This is also why m00labs is deliberately model-agnostic. The lab is not invested in one vendor's ecosystem. The choice between OpenAI, Anthropic and open-source models is made per engagement, on the merits of your data and constraints — and it is revisited as the models change.
What the lab reuses
- Integration patterns for the systems mid-market teams actually run — CRMs, support desks, document stores, spreadsheets and the APIs between them.
- Evaluation and test harnesses for AI behaviour, so changes are caught before they reach production rather than after.
- An operations baseline — error handling, logging, monitoring and alerting — that ships with every build.
- Handover documentation and operator walkthroughs, written so your team can run the system without a vendor on call.
The toolchain is a scoping decision
We name the stack in the quote, before work starts, so you know what you are approving.
Hosted models
OpenAI and Anthropic APIs where capability, speed and managed infrastructure fit the workflow — the default for language-heavy features.
Open-source models
Self-hosted models where data must not leave your environment, call volume changes the economics, or you need full control of the stack.
Orchestration and code
Workflow orchestration where a workflow engine fits the process; typed application code where it does not — or a clean combination of both.
Your data layer
How your data flows, where anything is hosted and who can access it are agreed and documented during scoping — before any build work starts. Integrations connect to your systems with scoped access.
Watch a workflow run.
Supplier invoices and orders become CRM records — extraction, a review step, a record created at the end. This is a simulated demo on synthetic data, not a client project; the process shown is the kind an Automation & Integration Sprint delivers.
Want to talk stack and scope?
Describe the systems you run and the workflow you want to improve.