Practical AI
that moves the number.

Senior staff spend hours each day searching wikis, copy-pasting between tools, and drafting routine responses.

A user works inside the tool they already use (browser, IDE, Slack, Teams). The copilot reads context from connected systems, drafts the response or action, and routes anything material for human approval before executing.

Embedded copilots that read context across your systems, draft grounded responses, and take governed actions — with citations and a reviewable trail.

Per-user permissions inherit source-system ACLs. Material actions require human approval. Every prompt, retrieval, and tool call is logged and reviewable.

Legal review is a bottleneck. Material risk lurks in clauses no one has time to compare across hundreds of agreements.

Contracts arrive (PDF, DOCX, scans, email). The system OCRs, parses, chunks, embeds, extracts key fields, compares clauses to your playbook, scores risk, and routes for approval — every step logged.

Domain-tuned extraction, semantic clause comparison, risk scoring, and approval workflows over your contract corpus — grounded in your playbook.

Permissioned access by matter, region, or business unit. Clause-level audit trail with reviewer attribution. Designed for secure private deployment.

Cross-system processes break at the seams. RPA handles clicks; it can't handle judgment, exceptions, or open-ended inputs.

A trigger arrives (email, form, system event). The orchestrator extracts intent, calls the right tools across systems, handles judgment-based decisions inline, and escalates to a named owner when the situation is outside policy.

Agentic orchestrators that reason about state, call APIs and internal tools, handle exceptions, and escalate to humans on edges.

Every tool call and decision logged with replay. Policy-aware routing. Configurable thresholds for risk, cost, and customer impact.

Policies, SOPs, technical documentation, and tribal knowledge live in PDFs, Slack threads, SharePoint, wikis, and someone's head.

Documents and structured sources are ingested on a schedule. A user asks a question through chat or in-context surfaces. The assistant retrieves, re-ranks, and cites the source — and refuses to answer outside its corpus.

RAG assistants over the full corpus — chunked, cited, permissioned, refreshed, and tuned to your domain. Answers come back with the source document and section.

Every chunk inherits ACLs from its source system. Citations link back to the source. Freshness windows and content owners are configurable.

One agent can plan or execute. Real work needs both — plus specialization, shared memory, and coordination across roles.

A supervisor agent owns the plan. Specialized worker agents own narrow capabilities (retrieve, extract, transform, call external tools). Shared memory and a typed tool registry keep coordination predictable and replayable.

Supervisor and worker topologies with shared memory, tool registries, replayable traces, and observability. Each agent has a narrow scope; the orchestrator owns the plan.

Replayable traces per workflow. Per-agent permissions and tool scopes. Cost and quality budgets enforced at the orchestrator.

Keyword search misses the question. Generic semantic search doesn't respect ACLs, freshness, or domain language.

A user query goes through a hybrid retriever (keyword + vector), is re-ranked against domain-tuned signals, and returns answers with source citations — refined conversationally where helpful.

Permissioned hybrid search with re-ranking, conversational refinement, and structured + unstructured results. Answers carry citations and stay inside policy.

ACLs respected at retrieval time, not after the fact. Freshness windows per source. Per-source disclosure controls for sensitive content.

Boards want an AI strategy. Most strategies are slideware that ignore data realities, talent, and platform constraints.

A short discovery maps current workflows, data realities, and existing systems. We deliver a ROI-ranked AI opportunity inventory, reference architecture, and phased roadmap — written for engineering and readable by the board.

Capability mapping, ROI-prioritized roadmaps, build-vs-buy decisions, and reference architectures that engineering can actually execute on.

Governance, audit, and data residency are part of the architecture — not a follow-up project. Recommendations include policy and review-trail design.