Forecasting
Demand, capacity and cash. Retrained on a schedule, monitored for drift, and honest about its confidence interval.
A demo takes a weekend. Keeping a model useful for a year takes evaluation, guardrails and someone on call. That is the part we build.
4 weeks
To a go / no-go call
Week 1
Evaluation harness
from $18k / mo
Sprint team
Four shapes this work usually takes. Most engagements are one of them, or two that grew together.
Demand, capacity and cash. Retrained on a schedule, monitored for drift, and honest about its confidence interval.
Intake, extraction and classification for the paperwork a team currently retypes by hand — with a review queue for anything it is unsure about.
Scoped to one job, grounded in your data, and measured on task completion rather than vibes.
Semantic search over your own corpus, with the retrieval quality measured separately from the generation quality.
The three things we will not negotiate away, because they are what keeps the build from unravelling later.
01
Scored test sets before a single prompt ships. Without them you cannot tell a regression from a bad day, and every change becomes an argument.
02
Review queues on anything irreversible. Automation earns its autonomy by clearing a bar you set.
03
Providers change pricing and deprecate versions. Swapping one is a config change, not a rewrite.
The specifics, so you can tell in ten seconds whether we cover what you need.
AI automation
Computer vision
Prediction & analytics
What we reach for
From the work
A claims team was retyping documents by hand. The model was the easy part — the evaluation harness and review queue around it are why it is still running fourteen months later.
See selected work →97.4%
Field accuracy
Then we say so at the week-four go / no-go call and you stop. You keep the evaluation harness and the written findings. We would rather lose the build than ship something that quietly degrades.
No. Your content is never used to train anything, by us or by a provider — we configure that explicitly and it goes in the contract.
Taking a task a person does by hand many times a day — reading a document, sorting a request, answering the same question — and having software do the routine cases while a person handles the rest. The gain is usually in the routing and the review queue, not in the model.
Yes: image and video analysis, object detection, OCR and quality inspection. Typical jobs are reading labels or documents, counting things, and spotting defects on a line. Where a camera and good lighting can be arranged, accuracy tends to be far higher than people expect.
Yes — grounded in your own documents and systems rather than general knowledge, with citations so an answer can be checked. We scope it to a defined job and measure whether it completes that job, because an assistant that is right most of the time about everything is worse than one that is reliable about something.
Yes, using open models hosted on your infrastructure, for cases where the data cannot leave. It removes a data-transfer problem and adds an operations one, and we will be straight with you about that trade-off before you commit.
Thirty minutes, no deck. You’ll get an honest read on scope, cost and whether we’re the right team for ai & ml.
Book a call →or email hello@bytiko.com