What we can build for you.

A knowledge assistant is a system that answers questions from your own material and shows where each answer came from. Helm Labs builds four shapes of it. The knowledge assistant retrieves across documents, tickets and wikis, with citations attached and permissions that follow your directory; it is also the usual fix for a Copilot rollout that isn't delivering. Customer and employee chat handles multi-turn conversations and escalates cleanly to a person whenever the question is sensitive. Semantic search finds by meaning across products, cases and documents, including recommendations, and needs no chat interface at all. Summaries and briefings condense meetings, long documents and daily operations into short written notes people actually read. All four sit inside the tools your teams already open. The Knowledge Assistants service is where this work gets scoped, and the service-desk assistant case study shows one running inside a live ticket system.

Document intelligence is the classification, extraction and review of the paperwork that blocks a decision: invoices, tenders, delivery notes, forms and specifications. Helm Labs builds three patterns here. Document extraction reads the file and writes into your ERP or DMS schema, validated field by field, with uncertain cases routed to a human reviewer rather than guessed. Classification and routing sends incoming email, tickets and documents to the right queue using confidence thresholds instead of assumptions. The analytics copilot answers plain-language questions against your database and backs every answer with a query you can open and inspect. The common thread is that a number nobody can verify is worse than no number at all. Accuracy is measured per field and reported. The Document Intelligence service covers scoping and rollout, and the tender analysis case study describes a pipeline reading 150 to 300 page tenders into a structured checklist.

A workflow agent is a system that takes an action inside your tools, not only an answer inside a chat window. Helm Labs builds three variants of it. Workflow agents triage the ticket, draft the reply and update the record, with human approval required wherever an action is irreversible and a complete trace retained for audit. Research agents run multi-step work across internal and external sources: gather, verify, then deliver a sourced summary. AI pipelines stay deterministic and use language-model steps only where they help, with retries, an audit trail and measurable quality per step. Tool access is scoped and reviewed before anything touches your ticketing, CRM or ERP. Nothing goes live without an approval queue and a rollback path. The Workflow Agents service sets out what one workflow running end to end includes, and our four-phase delivery process shows where the approval design happens.

Voice, vision and content systems handle the inputs that are not already clean text: phone calls, photographs, drawings, scans and templated writing. Helm Labs builds three of them. Voice agents take speech in, reason over it and answer in speech, working as phone and reception assistants with a live handoff to your team. Vision and multimodal systems read photos, drawings, scans and damage reports, then turn them into structured findings your other systems can store. Content generation drafts product texts, listings and reports in your own templates and tone; the output is reviewed rather than auto-published. All three follow the same rule as everything else we ship: a person stays in the loop wherever the result is external or irreversible. The wider solution catalog lists the adjacent patterns, and a short description of your workflow is enough for us to say whether one of these fits it.

An AI platform layer is the shared infrastructure between your applications and the models, so capability gets governed once instead of six times. Helm Labs builds three parts of it. The AI gateway gives every team one controlled entry point with budgets, audit logs, model routing and EU hosting, replacing six teams holding six API keys. Engineering copilots set AI-assisted development up safely for your own developers: codebase assistants, review workflows and a written usage policy. Evaluation and observability adds test sets, tracing and CI gates to the AI you already run, so quality is measured rather than asserted. This group is usually bought once the first integration is live and the second one is being planned. The AI Platform Layer service describes the deployed gateway and CI pipeline you end up owning, and the KPI monitoring case study shows traceability applied to reporting.