Automate the Work Your Team Shouldn't Be Doing
LLM applications and workflow automation that cut manual work in production.
- LLM application development
- n8n / Python workflow automation
- Retrieval and knowledge search
- Evals, monitoring & cost control
Automation that survives production
There is a graveyard of AI prototypes that worked in the notebook and fell over in the real world: answers that drift, integrations that break, costs that triple quietly. We build the other kind: LLM applications, workflow automation and retrieval pipelines that run in production, with evals and monitoring treated as part of the system rather than a launch-week afterthought.
Our approach is grounded in peer-reviewed machine learning research, T&M's technical leadership includes a published AI researcher, which matters less as a credential and more as a habit: we test claims before we ship them, ours included.
The build loop
Every automation engagement starts with the same uncomfortable question: what does this workflow cost you today, in hours or errors or missed revenue? If we can't beat that number, the project shouldn't exist. Scoping produces a written brief with the target metric in it.
Builds run in short cycles against an eval set, real documents, real queries, real edge cases from your business, so "it works" means measured retrieval accuracy and answer quality, not a happy demo. On IqbalAI we went further and made the engineering loop itself AI-native, auto-generating typed client contracts so the codebase couldn't drift from its API. Launch includes cost monitoring (token spend is a budget line, and it leaks if nobody watches it) and a handover doc your team can actually operate from.
What usually goes wrong
Two patterns kill most automation projects. First: automating a process nobody has written down, which means automating the confusion. We make you define the workflow before we touch it: tedious, and it's saved more budgets than any model upgrade. Second: skipping evals because the first ten answers looked right. Retrieval quality is a distribution, not an anecdote; without a test set you find the failures when your customers do. We don't ship without one.
Straight answers
Retrieval, fine-tuning, or just better prompts. Which do we need?
Usually retrieval first; it's cheaper, auditable, and your data stays current. Fine-tuning earns its cost in narrow cases. We'll tell you which during scoping, including when the honest answer is "a prompt and a spreadsheet."
Can you work with our existing stack: n8n, Zapier, internal APIs?
Yes. We build heavily on n8n and Python ourselves and run our own agency operations on the same automation we sell.
How do you keep costs from blowing up?
Model routing (small models for small jobs), caching, and a cost dashboard from day one. Token spend gets reviewed like any other budget line.
What about our data: privacy and access?
Your data stays in your accounts wherever possible; we work inside your cloud and sign NDAs as standard. Three years of passing healthcare data-protection audits on CaptureProof shaped how we handle everything else.
What does an engagement look like?
A scoping call, a fixed-price pilot on one workflow with a measurable target, then expand or stop. Most clients know within a month whether the numbers work.