Our approach works best when the task is narrow, the output is structured, and the volume is high. If you can describe the job in one sentence and check the answer, it's a candidate.
Emails, tickets, claims, requests → the right queue, priority, or category. The most common first pilot: a fixed label set, easy to measure, immediate labor savings.
Ticket routing · complaint triage · mailroom automation · content moderation
Invoices, KYC documents, contracts, lab reports, shipping papers → strict JSON matching your schema. Fine-tuned small models are especially strong here; they learn your document layouts.
Invoice fields · ID documents · contract clauses · clinical notes
Find and mask personal data before it reaches any downstream system, the model that protects your data must itself be local. Naturally pairs with the other use cases as a gatekeeper.
GDPR / 152-ФЗ / HIPAA pipelines · log scrubbing · data-sharing prep
Answer questions over SOPs, policies, product docs or maintenance manuals. Delivered with a local retrieval layer; the model is trained to answer in your style, cite the source, and refuse when it doesn't know.
Support desks · plant floor SOPs · HR / IT helpdesk · compliance lookups
Natural language → SQL over your schema, dialect and naming conventions. Small models fine-tuned on a specific schema routinely beat general frontier models on that schema.
Analyst self-service · BI copilots · report automation
Classify, cluster and summarize application, security or industrial logs on-prem, at volumes where per-token API pricing is prohibitive.
SOC alert triage · incident tagging · anomaly explanation
Narrow coding tasks tied to your stack: generate migrations from a spec, convert legacy formats, write tests in your conventions, review against your lint rules.
Legacy modernization · config generation · test scaffolding
Turn long inputs into fixed-format summaries: call notes, case files, meeting transcripts, in your template, with your terminology.
Case summaries · call wrap-ups · regulatory filings
Being honest about scope is part of the pitch.
We don't build general chatbots, open-ended writing assistants, or "an internal ChatGPT". Compact purpose-built models are excellent at one job and mediocre at everything else. That is the trade. If you need broad reasoning across arbitrary topics, a frontier model (via a private cloud tenant) is the right tool, and we'll say so in the first call.
Two to four weeks. One task. A measurable result before you commit.