AI readiness assessment
A structured review of your data, workflows, tooling, and governance, scored against five readiness stages.
For you if: you don't know where to start
DELIVERABLEREADINESS REPORT + STAGE SCORE
Each service has a named deliverable and a fixed fee quoted before work begins. Start with one service or combine the stages into a 90-day Foundation.
FIVE STAGES · ONE CLEAR DECISION AT A TIME
Each stage names what it produces, who remains accountable, and what must be reviewed before the work advances.
Before anything is built: find where AI can pay in your business and define the cost case.
A structured review of your data, workflows, tooling, and governance, scored against five readiness stages.
For you if: you don't know where to start
DELIVERABLEREADINESS REPORT + STAGE SCORE
We shadow your teams, map the workflows, and rank them by expected payback, including the work we recommend leaving alone.
For you if: you suspect AI could help but can't name where
DELIVERABLERANKED OPPORTUNITY MAP · FIXED FEE · QUOTED UPFRONT
For a chosen workflow: the operating design, data pipeline, and cloud-vs-local cost curve on one page.
For you if: you need a business case the board will read
DELIVERABLE1-PAGE ARCHITECTURE + COST CURVE
Your unstructured files become infrastructure: a vector database for meaning, a knowledge graph for facts.
Full ingestion of your files, shares, and mailboxes into a hybrid RAG stack: chunked, embedded, entity-extracted, cited.
For you if: answers exist somewhere but nobody can find them
DELIVERABLEVECTOR DB + KNOWLEDGE GRAPH
Continuous sync to reduce stale knowledge: connectors, change detection, and staleness monitoring.
For you if: you built RAG once and it quietly rotted
DELIVERABLELIVE SYNC + STALENESS ALERTS
We trace ten representative questions through your existing AI setup and show exactly where retrieval fails, and why.
For you if: you have AI but the answers disappoint
DELIVERABLERETRIEVAL GAP REPORT
Skills, tools, memory, approvals, and audit trails that let a model carry a workflow reliably.
A system engineered for your workflow, with model choices, approvals, an audit trail, and escalation built in.
For you if: generic AI employee products do not fit how you work
DELIVERABLEPRODUCTION WORKFLOW SYSTEM
Your procedures, written with your domain experts as versioned, testable skills, reusable across every AI employee.
For you if: your know-how lives in a few people's heads
DELIVERABLESKILL LIBRARY (VERSIONED)
Secure connectors into Microsoft Teams, Slack, and email, plus your CRM, ERP, and document systems as scoped tools.
For you if: AI should show up where work already happens
DELIVERABLECHANNEL + TOOL CONNECTORS
AI employees go to work inside your channels: supervised, cited, and measured against the workflow they own.
A supervised AI employee carries one workflow in one channel. Every consequential action requires human approval, and the log records what the AI employee flagged and the approver caught.
For you if: you want proof before commitment
DELIVERABLE6-WEEK SUPERVISED PILOT
Scale from one AI employee to a roster: shared skills, shared memory, per-team guardrails, and usage analytics.
For you if: the pilot worked and teams are asking for more
DELIVERABLEAI EMPLOYEE ROSTER DEPLOYMENT
Training for staff who will direct, correct, and audit AI employees after handover.
For you if: adoption is your bottleneck, not technology
DELIVERABLETRAINING + PLAYBOOKS
Run suitable models on your hardware to control high-volume costs and keep sensitive data on site.
Hardware sizing, procurement guidance, and open models tuned to your workload, run entirely on premises.
For you if: metered bills or data egress keep you up at night
DELIVERABLEON-PREM INFERENCE STACK
Routing between local and frontier models per task: privacy and cost locally, peak capability when it pays.
For you if: you need local cost control and frontier capability
DELIVERABLEMODEL ROUTER + POLICY
We run what we built (monitoring, model upgrades, skill maintenance) until your team is ready to take the keys.
For you if: no in-house ML ops team (yet)
DELIVERABLESLA-BACKED OPERATIONS · QUOTED UPFRONT
We run assessment through deployment with one accountable team and milestones you can hold us to. We take a maximum of two Foundation engagements at a time, so the founder remains directly involved in every one.