// ai_enablement

AI, but actually
in production.

The gap between an impressive demo and an AI system your org actually runs on is wide, and most orgs never cross it. We do the three things that close it: deploy the right tooling, build what doesn't exist yet, and train the humans who'll live with it.

// the_three_pillars

Tooling. Build. Training. One practice.

// ai_tooling

AI Tooling

Getting the off-the-shelf stack deployed, configured, and actually used. Claude, GPT, Gemini, Cursor, the platform tools -- into your workflow, not as a sandbox tab nobody opens.

  • ›Tool selection and stack design
  • ›Security + data governance setup
  • ›Workflow integration with your systems
  • ›Rollout and adoption playbooks
// ai_build

Custom AI Build

What you need doesn't exist yet, or the off-the-shelf version is too generic to move the number. We architect, build, and maintain the thing.

  • ›Custom agents with scoped tool access
  • ›RAG pipelines over your proprietary data
  • ›Evals, guardrails, observability
  • ›Versioned, tested, production-ready
// ai_training

AI Training

The tools only produce value if your team uses them well. Role-specific training that turns skeptics into operators and operators into force multipliers.

  • ›Role-specific curriculum (ops, sales, legal, exec)
  • ›Hands-on workshops, not webinars
  • ›Prompt libraries for your actual tasks
  • ›90-day adoption tracking
// problems_we_fix

The boring operational problems where AI actually pays for itself.

Your CRM data is a graveyard

We build AI pipelines that clean, enrich, and resurrect contact data without hiring offshore data entry.

Support tickets pile up

Triage agents that classify, route, and draft responses -- your humans only touch what needs a human.

Compliance review is a bottleneck

AI-first drafts of policy checks, contract review, and disclosure diffs -- reviewed by counsel, not authored by counsel.

Reports take a week to produce

Data-to-narrative pipelines. Monday morning you get the report, not a request for one.

// what_we_bring

The stack.

LLM Integration
Agent Architectures
RAG Pipelines
Evals & Guardrails
Team Training
Rapid Prototyping
// before_after

What “in production” actually looks like.

Task
Before
After enablement
Lead follow-up
Reps batch-email 3 days after sign-up, 60% go cold.
Agent drafts a personalized first touch in under 10 minutes, rep reviews and sends.
Internal knowledge retrieval
Slack search, Confluence hunt, ask the one person who knows.
Ask-in-natural-language over every doc, meeting transcript, and ticket. 3-second answer with citations.
Monthly board report
Analyst spends 3 days assembling, 1 day writing, reads half the source docs.
Draft assembled overnight from actual data. Analyst edits and adds judgment, not plumbing.
Customer onboarding
Generic 5-email drip. 22% activation.
Agent watches early usage, sends contextual guidance. 48% activation.
// faq

What you're probably thinking.

We've done pilots that went nowhere -- why will this work?

Pilots die because they're pilots. We don't run pilots. We run narrow, production-deployed slices with measurable outcomes from day one. If it doesn't move a number, we don't keep it.

Who owns what we build?

You do. Code, prompts, evals, pipelines -- delivered in your repo under your infra, with documentation your team can actually maintain. Our retainer is for evolution, not lock-in.

Can you work inside our security boundary?

Yes. We've shipped inside SOC 2, HIPAA, and FINRA-adjacent environments. Private models, on-prem inference, PII scrubbing, role-scoped tool access -- all standard patterns for us.

Do you replace my team?

No. We raise their ceiling. The best results we see are when our work is inherited by an in-house team that now operates at 3-5x their old pace. That's the entire point.

Every pilot has been a pilot.
Let's put one in production.