Document & data workflows
Extraction, classification and routing across messy real-world documents — contracts, invoices, specifications, regulatory filings. The kind of input that quietly breaks a naive pipeline.
AI engineering studio
Agents that do the work — not just the demo.
We design, build and operate AI agent systems that automate real business processes. Then we measure them until they are reliable enough to trust.
What we build
Four areas where agents reliably earn their keep.
Process automation
Agents that drive the tools your team already uses — CRM, ERP, ticketing, spreadsheets, internal APIs — and hand back to a human at exactly the points where judgement is required.
Domain search & retrieval
Retrieval that finds the right record in large, structured, domain-specific catalogues — where off-the-shelf embeddings tend to return plausible nonsense with total confidence.
Evaluation & observability
Curated ground truth, regression suites, cost and latency tracking. Without these you cannot tell an improvement from a regression — only that something changed.
How we work
Short loops, real data, no theatre.
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Map the process first
Before a model is chosen, we walk the actual workflow and find where the hours go. Sometimes the answer is not an agent at all — and we will say so.
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Prove it on your data
A narrow pilot against your real documents and your real edge cases. If the accuracy is not there, you find out in weeks rather than quarters.
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Ship it into production
Deployment, monitoring, cost ceilings, retries and fallbacks for the day a model provider degrades. This is the stage where most pilots quietly die.
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Keep it honest
Regression tests against curated ground truth, so quality cannot drift silently while everyone assumes it is still fine.
How we think about it
Reliability is an engineering problem, not a prompt problem.
Model quality sets the ceiling. Everything underneath it — retries, timeouts, idempotency, fallbacks, cost control — is ordinary distributed-systems work, and it decides whether a system survives contact with production.
Measure before you optimise.
A system without a ground-truth dataset cannot be improved, only changed. We build the measurement before we start tuning the thing being measured.
The unglamorous failures are the expensive ones.
Provider outages, queue backpressure, silent data drift, a pipeline that returns zero rows without raising an error. Not the demo — the Tuesday afternoon three months later.
Have a process worth automating?
Tell us what your team repeats every day. If agents are the wrong tool for it, we will tell you that too.
hello@logsbery.com