Real transformations,
told in chapters.
Forward-thinking teams partner with us to make AI a real driver of growth — not a science project. Below: three engagements, from first whiteboard to live system — with the process, the stack, and the numbers that came out the other side.
From 40 years of PDFs to a Knowledge Management System that answers in seconds.
A century-old industrial group was drowning in tribal knowledge — SOPs, safety manuals and engineering notes scattered across SharePoint, email and people's heads. We built a grounded RAG-based KMS that turned 1.2M documents into a single, trusted answer surface.
New engineers took 9+ months to ramp up. Field technicians made costly mistakes searching the wrong revision of a manual. Compliance audits required weeks of manual document retrieval.
- Week 1–2 · Step 01Discovery & knowledge audit
Mapped 14 source systems, interviewed 23 SMEs, identified the 8 highest-value knowledge workflows.
- Week 3–5 · Step 02Ingestion pipeline
Built versioned ingestion for PDFs, CAD notes and Confluence. Document-aware chunking preserved tables, diagrams and section hierarchy.
- Week 6–8 · Step 03Grounded retrieval & evals
Hybrid BM25 + vector retrieval, re-ranking, and a citation-first answer model. Built a 600-question eval set with SMEs to score factual accuracy weekly.
- Week 9–11 · Step 04Agentic workflows
Layered specialized agents on top: a Safety agent that refuses outdated revisions, a Compliance agent that auto-assembles audit packs.
- Week 12–14 · Step 05Rollout & enablement
Phased launch to 400 → 4,200 users with feedback loops wired directly into the eval set. Self-improving from day one.
"It's the first time in 40 years our institutional memory actually answers back."
An LLM copilot that turned 45-minute portfolio reviews into 4-minute conversations.
Helix's advisors spent half their week assembling portfolio reviews by hand. We shipped an in-product copilot grounded on each client's holdings, household goals and market data — drafting reviews, suggesting rebalances, and explaining its reasoning.
Advisors were the bottleneck. Compliance forbade generic LLM use, and every recommendation had to be traceable to source data and within firm policy.
- Week 1–2 · Step 01Compliance-first design
Co-designed the trust model with the CCO: every output cites sources, every action requires advisor approval, every prompt is logged.
- Week 3–5 · Step 02Grounded context layer
Connected the portfolio system, CRM and research feeds via a unified context broker. The model never sees raw PII it doesn't need.
- Week 6–8 · Step 03Copilot UX in-product
Embedded inside the existing advisor workstation — side-by-side draft, citation hover, one-click insert. No new tool to learn.
- Week 9–10 · Step 04Evaluation & rollout
Shadow-mode for 2 weeks against 1,400 historical reviews; then graduated rollout to all 180 advisors with weekly eval drift checks.
"Compliance signed off on day one because the trust model came first, not last."
A multi-agent system that runs back-office operations across 60 countries — overnight.
Northwind processed 12,000 exception cases a quarter by hand: customs mismatches, invoice errors, missed SLAs. We deployed a multi-agent system that triages, drafts, and resolves cases autonomously — escalating only the edge cases.
The work was high-volume, multi-system, and unforgiving. Any wrong action could cost $10K+. They needed autonomy with airtight guardrails and full audit trails.
- Week 1–3 · Step 01Process mining
Instrumented the existing workflow to learn from 90 days of human resolutions. Identified the 12 case patterns covering 84% of volume.
- Week 4–7 · Step 02Agent fleet architecture
Built specialized agents — Triage, Investigator, Drafter, Approver — coordinated by a planner with tool use across 6 internal systems.
- Week 8–11 · Step 03Guardrails & human-in-the-loop
Hard policy gates for any action above thresholds. Every agent decision is reasoning-logged and replayable.
- Week 12–14 · Step 04Continuous evals
Built an eval suite from real historical cases, scored nightly. Regressions block deployment automatically.
- Week 15–16 · Step 05Production rollout
Started at 5% of inbound cases, scaled to 70% over 8 weeks based on accuracy and CSAT.
"Our ops team stopped firefighting and started designing the next layer of automation."
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