We help real-economy companies put AI to work. Our engineers pair deep operating expertise with hands-on delivery to turn messy operational problems into intelligent workflows that drive measurable impact.
There are two kinds of service businesses now: those being transformed around AI, and those being replaced by it.
We do the transformation.
HOW WE WORK
Our engineers and AI strategists learn how the work really gets done: who does what, where things slow down, what goes wrong, and which decisions matter. We map the work and identify where AI can have the highest impact.
We build production AI systems that take on real operational work inside the tools your team already uses, with clear ownership through launch and adoption.
We guide the rollout from start to finish, training teams, defining new ways of working, and building the feedback loops that turn a launch into lasting adoption.
IMPACT
Real outcomes from production systems doing real operational work.
Manual work reduced
68%
average reduction in manual processing time across automated back-office workflows.
Faster deployment
40%
faster path to production for portfolio companies using shared components.
Portfolio scale
60+
companies aligned around one reusable AI transformation playbook.
We build AI operating systems that become the backbone of the business, not standalone software that creates one more thing for your team to manage.
Our engineers use proven delivery patterns and reusable components to move from a defined problem to a live system in weeks instead of months, while keeping the controls the work requires.
A system only creates value when people use it. We take responsibility for the rollout, training, operating model, and feedback loops that turn a launch into lasting change.
The hard part of AI was never the model. It's the messy stuff around it: competing priorities, teams that have been burned by a failed pilot before, and the change management nobody budgets for. We've watched this stall good projects inside both large enterprises and mid-market portcos, and we built Methodic to handle it head-on.
A mid-market PE fund with 15 vertical SaaS portfolio companies (healthcare, compliance, field services, workforce management) had significant operational inefficiency in the back offices of every portco. Manual AP processing, compliance reporting, invoice reconciliation, and internal ops workflows were consuming FTE capacity that should have gone toward growth. Each company was solving the same problems in isolation, with no shared playbook.
We ran a portfolio-wide workflow diagnostic to map the highest-friction back-office processes across all 15 companies and rank them by automation potential and ROI. Then we deployed in waves, building and shipping AI-powered automation for AP intake and routing, compliance report generation, contract data extraction, and internal ops, including agents that handle exception routing end to end. Each build was designed as a reusable component so subsequent portcos got to production faster than the first. We stood up a governance framework and an AI operating model across the full portfolio in Month 2.
A 40-location specialty care group was processing 8,000+ invoices per month manually across a fragmented AP function. Compliance reporting for payer contracts required 3 FTEs working full-time on data reconciliation. Leadership had identified the problem but had no internal AI capacity to address it.
We mapped the full AP workflow, identified the four highest-friction steps, and built an AI-assisted invoice intake, classification, and exceptions-routing system integrated directly into their existing ERP, with an agent handling exception triage autonomously. Separately, we built an automated payer compliance report generator pulling from their existing data warehouse. We trained staff in two days. Total build time: 7 weeks.
A B2B SaaS company with 60 engineers was watching competitors ship faster. Leadership had budget for AI tools but no structured rollout. Previous attempts at Copilot adoption had stalled at 20% active use after 90 days. Feature cycle time was too slow and the team was skeptical.
We ran a two-week diagnostic to understand where the team spent time and where the real friction was. Built a per-team onboarding curriculum anchored to actual workflows, not generic prompts. Deployed Copilot and Cursor with governance guardrails, stood up an internal AI Champion program, and built a code review agent that cut PR review time. Active adoption hit 80% within 8 weeks.
We work with companies that make, move, sell, and service the things the world runs on, including industrial operations, field services, food, retail, and logistics. That's where better workflows turn directly into operating leverage.
Ten portcos, ten different starting points, one hold-period clock. See our PE playbook →
Tell us what you're trying to build and where it's stuck. A practitioner, not a sales rep, will get back to you within one business day.
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