Solutions

We don't sell services.
We solve patterns.

Every company we work with has one of three problems. Find yours below — we'll show you exactly how we solve it.

Don't see your pattern? Let's talk.
The Pattern

Your team is doing work a system should handle. The requests follow a pattern. The responses follow a pattern. But someone sits there doing it manually because no one has built the system. Your people are smart — they're stuck doing dumb work. Data entry, triage, routing, formatting, status updates. Meanwhile, every new customer means more manual work, more coordination, more hires. Your unit economics are flat because humans don't scale linearly.

You'll know this is you if...
  • Your team says "we just need more people"
  • The same process runs 50+ times per week
  • New hires spend weeks learning tribal procedures
  • Errors come from fatigue, not incompetence
  • Your ops headcount tracks revenue almost 1:1
The Playbook

AI Triage & Task Automation

We identify the 3-5 workflows consuming the most skilled-human-hours on pattern-following tasks. Then we build AI that handles the predictable 80% autonomously and routes the 20% that needs real judgment to the right person, with full context.

Embedded observation with your ops team. We time every task, map every decision point, and identify where pattern-work is hiding inside "complex" processes.

AI classification layer that sorts, routes, and pre-processes incoming work. Rules engine for the obvious stuff, LLM for the nuanced stuff. Confidence thresholds so it knows what it doesn't know.

Exception routing with context. When AI can't decide, it packages everything the human needs to decide in seconds, not minutes. Every human override trains the system.

Real-time visibility: what AI handled, what got escalated, where accuracy is trending. Your team sees the system learning. Trust builds through transparency.

Timeline

Working prototype in 10-14 days. Production in 8-10 weeks.

Sound like your situation?

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The Pattern

You have years of customer interactions, operational history, domain expertise buried in spreadsheets, email threads, and tribal knowledge. You know it's valuable. Every "data strategy" conversation ends with a proposal for an 18-month data warehouse project. Meanwhile, your competitors with worse data but better AI access are moving faster.

You'll know this is you if...
  • Your team says "we should be using our data better" at least once a month
  • New hires take 6+ months to ramp because the knowledge isn't accessible
  • Your last data initiative produced a dashboard nobody checks
  • Institutional memory walks out the door with every departure
The Playbook

Conversational Intelligence Layer

We build an AI layer that sits on top of your existing data — messy as it is — and makes it queryable, actionable, and useful through natural conversation. No data warehouse required. No 18-month migration. Your team talks to your data like they'd talk to your most knowledgeable colleague.

We connect to your existing sources without moving or transforming anything. Databases, spreadsheets, documents, emails — we work with the mess as-is.

Smart chunking and embedding. We optimize for how your team actually asks questions, not how the data is structured. Hybrid search: semantic understanding + keyword precision.

Your team asks questions in plain language. The AI retrieves relevant context, synthesizes an answer, and cites its sources. Accuracy is verifiable, not black-box.

Beyond answers: the AI triggers workflows. "Create a ticket for this," "Update the CRM," "Draft a response." Intelligence becomes action.

Timeline

Connected to data in 3-5 days. Usable prototype in 2 weeks. Production in 5-6 weeks.

Sound like your situation?

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The Pattern

You've seen the demos. They were impressive. Then came the "integration phase" that turned into a "discovery phase" that turned into a "let's revisit scope" meeting. The vendors aren't lying — their product works. It just doesn't work for YOUR workflow, with YOUR data, in YOUR stack. Or your team can build but AI is a different discipline. The landscape changes every 3 months and every blog post contradicts the last one.

You'll know this is you if...
  • You have 3+ vendor proposals collecting dust
  • Your team is skeptical that "AI actually works"
  • The phrase "integration timeline" makes your CTO twitch
  • Your engineers are excited about AI but paralyzed by options
  • You've started 2+ internal experiments that fizzled
The Playbook

AI Rescue & Architecture Sprint

We audit what was attempted, identify exactly why it stalled, and build the thing that should have been built 6 months ago. From your data, on your infra, owned by your team. We work alongside your engineering team — we design the system, make the build-vs-buy decisions, and pair-program the critical paths. When we leave, your team owns everything.

We review every failed or stalled initiative. Not to assign blame — to extract signal. What did the vendor get right? Where did integration break? What assumptions were wrong? Every decision documented with rationale.

We map what your team actually does vs. what the vendors assumed they do. The gap between these two is almost always where the project died.

Custom-built on your existing data pipes and infrastructure. No new platform to adopt. No migration. We use what you have and fill the gaps with purpose-built AI. Your engineers build core components with our guidance.

Your engineers pair with ours. By handoff, they understand every component, can debug it, extend it, and maintain it without us. Runbooks, decision logs, architecture docs, plus 30 days of async support.

Timeline

Audit complete in 48 hours. Working prototype in 2 weeks. Team autonomous in 4 weeks.

Sound like your situation?

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How We Execute

Every engagement follows the same structure.

Engagement

Week by week.

Wk 1-2
Wk 3-8
Wk 9-10
Weeks 1-2

Observe

  • Embed with ops team
  • Map workflows + friction
  • Audit tools and integrations
  • Build 2-3 proofs of concept
  • Go/no-go decision
You have
Working prototypes against real data. A clear picture of what to build.
Weeks 3-8

Build

  • Production AI workflows
  • Integrations to your tool stack
  • Security scanning across 25 controls
  • Deploy to managed infrastructure
  • Monitoring, alerting, dashboards
  • Team using workflows daily by week 5
  • Iterating on feedback in real-time
  • Documentation and runbooks
You have
Production systems on managed infrastructure. Your team is already using them daily.
Weeks 9-10

Operate

  • Knowledge transfer
  • Performance baseline
  • Incident response handoff
  • Monthly review cadence
  • Full git export
You have
A running system with monitoring, support, and a clear path to expand.
Built on our open-source stack →
Risk Model

Built to de-risk the decision.

Every engagement is structured so you see results before committing, own everything we build, and can walk away at any checkpoint with working systems.

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Fixed scope, fixed budget
Every engagement has a defined deliverable count, timeline, and price. No open-ended retainers. No surprise invoices.
Go/no-go at week 2
After the observe phase, you see working proofs of concept. If the fit isn't there, you stop. You've spent 2 weeks, not 6 months.
You own everything
All code, all state, all integrations. Git-backed, fully exportable, self-hostable. The IP is yours from day one.
Open source stack
Upjack, mpak, and Platform are open source. No vendor lock-in by architecture. Inspect, fork, or self-host at any time.
Honest kill signals
If a workflow isn't working, we say so. Monthly reviews include what to kill, not just what to expand. No sunk-cost logic.

See what a pilot looks like for your business.

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Or email directly: hello@nimblebrain.ai