Stop paying for AI nobody on your team knows how to use .
We embed LLMs and intelligent automation into the tools your team already opens every morning — not a separate chat window they'll forget about by lunch.
The gap between buying AI and using AI is widening.
of companies report AI adoption, but only 26% generate measurable value from it.
productivity gains documented on the right narrow workflows — not the broad 'AI strategy.'
of internal AI pilots never make it to production. Pilot fatigue is real.
from kickoff to a working pilot when we scope the workflow first, not the model.
Sources: BCG AI at Work 2024, McKinsey State of AI, Gartner AI Pulse.
If three of these sound like your team, you have AI-shaped work waiting.
We use this checklist on the first call. It separates real automation candidates from shiny-object pilots that don't survive contact with payroll.
- 01Your team copy-pastes the same data between three systems every day.
- 02Customer emails get triaged manually because nobody trusts the inbox rules.
- 03You pay for a ChatGPT Team or Copilot license that ~3 people actually use.
- 04Sales reps rewrite the same proposal sections in every deck.
- 05Support agents answer the same five questions from a buried knowledge base.
- 06Reporting requires someone to 'pull the numbers' before any leadership meeting.
- 07You ran an AI pilot last year and quietly stopped talking about it.
- 08Leadership wants 'AI on the roadmap' but nobody can name a real use case.
A four-week path from messy workflow to production-grade assistant.
Workflow shadowing
We sit with the operators doing the work — not just the executives describing it — and chart every handoff, decision, and bottleneck.
Opportunity scoring
Each workflow gets scored on time saved, error rate, and AI fit. We kill the bad bets before they become pilots.
Pilot in production
A working assistant wired into your real stack inside 2–4 weeks. No sandbox demos that don't survive contact with reality.
Guardrails & evals
Prompts, retrieval, evaluation harness, and human-in-the-loop checkpoints so the assistant fails safely and visibly.
Team enablement
Operator training, runbooks, and an internal champion identified so adoption doesn't depend on us.
Measure & iterate
Weekly metrics, monthly retros, and quarterly expansion — you see the ROI in dollars and hours, not vibes.
Pilot, partner, or platform — pick the depth you need.
Single workflow pilot
- One high-ROI workflow, picked together
- Working pilot in 2–4 weeks
- Evaluation harness + safety guardrails
- Team training + adoption playbook
- Production handoff or scoped extension
Quarterly AI roadmap
- Everything in Spark, on rotation
- Rolling backlog of automations + assistants
- Dedicated AI engineer + product partner
- Monthly ROI report to leadership
- Evals + monitoring on everything shipped
Build it once, use it everywhere
- Internal AI platform: prompts, retrieval, evals, guardrails
- Reusable components across departments
- Admin console + observability dashboards
- Security, access, and audit controls
- Optional Partner plan for ongoing iteration
Real assistants. Real integrations. Real numbers.
- Workflow audit and AI opportunity map
- Production-ready integrations with your stack
- Custom prompts, guardrails, and evaluation harness
- Team training and runbooks
FAQ.
Which models do you use — and do we have to commit to one?＋
We're model-agnostic. We pick per workflow based on accuracy, latency, and cost — usually a mix of frontier models (GPT, Claude, Gemini) and smaller open-weight models for narrow tasks. You don't get locked into a vendor.
Will our data train someone's model?＋
No. We default to enterprise / no-train endpoints, and for sensitive workloads we route through your own cloud account. Data handling is documented and reviewable before anything ships.
What about hallucinations?＋
Every assistant ships with retrieval grounding, an evaluation harness against real examples, and human-in-the-loop checkpoints on anything that touches customers or money. We design for failure modes, not for demos.
Can you integrate with [our weird internal tool]?＋
Almost certainly. If it has an API, a database, or even a sane export, we can integrate. Part of the discovery phase is identifying integration risks before we commit to a delivery date.
What if AI just isn't the right answer?＋
We'll tell you. Half of our wins are 'this should be a deterministic automation, not an LLM.' Our job is the outcome, not the buzzword.
Stop running your business around your tools.
Get a free AI Assessment and walk away with a clear, prioritized plan for where automation will actually move the needle — typically in 30 days or less.
Free, no pitch. Just an honest look at what's worth automating.