We design and ship enterprise-grade GenAI — document intelligence, retrieval-augmented chat, and automated reporting — with citations, permissions, and evaluation built in. Integrated into Fabric, Power BI, SharePoint, and the tools your team already uses.
Most enterprises have already done the demo. The hard part is wiring AI into the systems where decisions actually get made — with the permissions, citations, and evaluation an auditor can sign off on.
The chatbot writes confident answers about your business, but you can't tell which document it pulled from — or whether the user was even allowed to see it.
Policies, contracts, SOPs, board minutes — thousands of documents that nobody reads twice. The institutional memory exists; it just isn't searchable.
Without faithfulness checks, citations, and an evaluation set, every model upgrade is a coin flip. Risk and legal won't sign off — and they shouldn't.
From a citation-grounded SOP copilot to invoice extraction with a human-in-the-loop queue — the work below shows up as production software your team can defend in an audit.
Question-answering grounded in your documents, with citations to the source paragraph and access controls inherited from the source system.
Field extraction from PDFs, emails, and forms with confidence scores, validation rules, and a review queue for low-confidence cases.
KPI commentary, board briefs, audit findings, and executive summaries generated in your brand voice from governed data sources.
Agents that read your data, call your systems, and draft work for human approval. Scoped tools, audit logs, no surprises.
Golden sets, faithfulness scoring, and regression tests so model upgrades are graded — not guessed at.
PII redaction, prompt-injection defenses, content filters, and full audit logs on every prompt, retrieval, and response.
Wired into Fabric, Power BI, SharePoint, Teams, and the SaaS apps your team already lives in. Outputs land where work happens.
Model routing, caching, rate-limits, and a usage dashboard so cost stays predictable and the right model handles the right job.
The reference architecture for our RAG-based copilots. Each layer is replaceable, every step is logged, and access controls inherit from the source system — not bolted on after.
We pick one well-bounded use case, ship it end-to-end, and use what we learn to scope the next two. The discovery workshop costs a day; everything else is paid.
Half-day discovery: data, decisions, risk appetite, success metric. Out: a shortlist with effort vs. impact.
Pick the first use case. Define the prompt contract, the eval set, and what "good" looks like — in writing.
Ingest, index, prompt, integrate. Hooked into your tenant, your access controls, and your evaluation set on day one.
Live with a real user group. Measure faithfulness, usefulness, and refusal rates. Tune what's tunable, document the rest.
Eval pipeline, drift monitor, named owner. The copilot still works — and is still safe — in month nine.
Outcomes worth aiming for from a focused AI enablement engagement. Magnitudes vary by corpus and domain; we'll baseline yours during the workshop.
Every response cites the source paragraph. Users can click through, verify, and trust the output — or call it out.
Drafting briefs, summarizing tickets, finding the policy clause — the small daily friction that GenAI is actually good at.
The contract corpus, the SOP library, the board-meeting archive — finally answering questions instead of taking up storage.
Every prompt, retrieval, and response logged. Permissions enforced at retrieval. Risk and legal can sign off, because the system shows its work.
Reusable ingestion, eval, and orchestration. The third copilot ships in weeks, not quarters — because the platform is already there.
A six-week engagement: one well-scoped copilot in your tenant, integrated with your data, evaluated honestly, and ready to defend in front of risk and legal.
Engagements scoped per use case. Fixed-price options available for the pilot; T&M for the second copilot and platform build-out.
The questions we get most often during scoping calls. If yours isn't here, write to info@arkimetrix.com.
No — you have options. For most clients we use Azure OpenAI or another managed model inside your tenant; nothing to operate on bare metal. When data residency, sovereignty, or cost economics call for it, we also deploy open-source models (Llama, Mistral, Qwen, and similar) on your on-prem GPU servers or on Arkimetrix-managed secure infrastructure. Same RAG pipeline, same eval framework, same governance — different runtime. We'll model the trade-offs (latency, quality, $/token, ops burden) before you commit.
Three things, in order: ground every answer in retrieved passages with citations, run an evaluation set on every prompt change with faithfulness scoring, and refuse confidently when retrieval comes back thin. The combination is what turns a demo into something risk and legal will sign off on.
Only if you want it to. Three deployment patterns, your call: (1) managed cloud — documents in your storage, embeddings in your vector index, model called inside your Azure / AWS region; (2) on-prem — the entire stack (vector DB, orchestration, open-source LLM) runs inside your data centre, air-gapped if needed; (3) Arkimetrix secure hosting — dedicated tenant on our SOC 2-aligned infrastructure for clients who want isolation without standing up GPUs themselves. We sign DPAs, configure retention, and document data flow end-to-end in every pattern.
We score candidates on three axes: data readiness, decision frequency, and downside if the model is wrong. The first copilot should be high frequency, well-bounded, and forgiving — not the highest-stakes thing on your list. The hard stuff comes second, on a platform that already exists.
Yes — most of our engagements are co-builds. We bring patterns, prompts, and a senior pair-programmer for your engineers. By the end, your team owns the eval set, the prompts, and the operational runbook.
Toronto, Canada and Pune, India. One team, two time zones. Most clients see this as a feature, not a bug — coverage runs nearly around the clock.
A 30-minute scoping call. Bring the document corpus or workflow you've been wanting to make searchable. We'll tell you whether GenAI is the right tool, and what we'd build first.