74% of companies still can't turn AI into real value. Only 26% get past pilots to tangible returns.
The missing piece is almost always the data foundation underneath. We stand up the warehouse, connect every system, and layer intelligence on top once the base is solid — built by founding engineers, not associates. Especially fluent with multi-entity operators and PE-backed platforms growing through acquisition.
You can't buy your way past a missing foundation. Here's the climb that actually moves EBITDA, and the two tiers we run in parallel to get you there.
Jumping straight to Tier 4 agents on Tier 1 data — "AI on top of the ERP," acting on a partial view.
Run Tiers 2 & 3 together. Document wins land now while the warehouse gets built underneath.
Staff already paste into ChatGPT on their phones: ungoverned value and real IP leakage. Don’t ban it; harvest it as a demand signal for where the ROI is.
Licensed assistants like M365 Copilot, Einstein, or ChatGPT Enterprise. No integration. Individual time savings on drafting and summarizing. Changes nobody’s P&L.
"Ask the company anything," grounded in your own spec libraries, submittals, manuals, and past bids. Captures tribal knowledge before the retirement cliff. Cheap, high-ROI, and runs in parallel with the build.
One source of truth across every operating company: procurement consolidation, pricing consistency, cross-sell, win-loss, consolidated FP&A. The real starting line for AI leverage, and where most roll-ups can’t go because they never built the layer.
Agents that act, not just inform: RFQ to drafted quote, PO reconciliation, pricing-anomaly flags, an auto-generated board pack. Needs Tier 3 data, write-access, and a human in the loop.
The data and agent layer becomes a reusable, portfolio-level asset that every new acquisition plugs into. AI is the value-creation thesis itself, not a feature.
Tiers 2 & 3 are where we start, built in parallel, not in sequence.
Start where it makes the most sense for your business. Most engagements open with foundation work, then settle into a lighter retainer as your team takes the wheel — the footprint shrinks as your data gets more trustworthy.
The warehouse, the infrastructure, the platform, the IP — client-owned from day one. We're not resellers and there's no black box. We set up the architecture, and it's yours.
We work as long as we're useful and we're happy to work ourselves out of a job. Start when it makes sense, stop when you don't need us. No renewals theater.
We stand up one warehouse that every entity flows into, define each metric exactly once, and turn each new acquisition from a project into a 1–2 day onboarding. It's built on infrastructure you own outright.
100% owned by you· Vendor-agnostic by design· One number in every dashboard, report & AI answer
Once the foundation is solid, AI moves from analysis to action. One everyday action — a quote, a record, a ticket — becomes intelligence that pays off in four directions at once. Same governed data underneath, so every answer agrees.
Every action becomes intel for the whole platform — not just one branch.
Every engagement below shipped to production — no prototypes, no proofs-of-concept collecting dust. Clients are described by shape, not name.
Replaced a legacy SQL Server reporting environment with a Snowflake + dbt warehouse, a governed semantic layer, custom BI, and an embedded AI analyst in Slack.
Migrated a vendor-locked chatbot for 300+ technicians onto an owned RAG stack — semantic retrieval, transparent reasoning, SME-managed knowledge base.
Built auth, observability, sandboxed execution, and evals — taking an enterprise from scattered AI demos to production-grade agents with governance.
Deployed a Slack- and Notion-connected agent for a senior partner — synthesizing portfolio status, strategy docs, and updates on demand.
Vetted the neocloud GPU market, brokered a partnership, and stood up an always-on cluster for custom model training and inference.
Migrated an entire org off a homegrown tool onto ChatGPT Enterprise via hackathons, workshops, and a grassroots champion network — not a top-down mandate.
Same problem, four very different shapes of help. Here's how we compare on the things a PE platform actually cares about.
| Relic AI | Big 4 / McKinsey | Internal hire | Dev shop | |
|---|---|---|---|---|
| Time to value | 2–4 weeks | 6–12 months | 3–6 months | 2–4 months |
| Builds things | Yes — it's all we do | Rarely | Yes | Yes |
| AI + data together | One team, one stack | Separate teams | Maybe | Code only |
| Exec partnership | Embedded founders | Advisor only | One person | No |
| Understands PE M&A | Built for it | Some | No | No |
| You own the stack | Always — no lock-in | No | Yes | Sometimes |
We intentionally limit how many companies we work with at once. If we're a fit, we go deep — not wide. No pitch, no pressure: just founding engineers who want to understand your problem.