We configure and connect your business systems with AI doing the programmatic work: the schema, the picklists, the layouts, the workflow rules and the integrations written as code, applied, read back and verified. The build takes days and the price is fixed.
The design stays with people: which system of record owns each fact, what a stage means, who approves what. The clicking does not. That is the difference between AI consulting and AI implementation services: we deliver the configured system.
Weeks of clicking through settings screens become days of verified runs. The model is written once; the AI applies it to every module, field and rule in one pass and reads each one back.
The outcome is quoted, not the hours. The estimate is guaranteed: if we estimate low, we absorb the difference. Discovery is no-risk, so the first number you see is one you can budget against.
Every write is read back and compared with the model. A mistake is a diff on a screen before production sees it, not a surprise in a report three weeks later.
The same sequence for a CRM, an ERP, a help desk or a marketing automation platform: the AI does the programmatic work at every step, and you hold the decisions.
We read your systems as they are: the metadata of every module, field, picklist, layout and rule, and your records with it.
The target design, written down as data: fields, picklists, layouts, workflow rules and integrations, reviewed and approved by you before anything changes.
A diff between the model and the live system: every create, update and retirement listed, with nothing applied yet.
The plan runs in a sandbox first, in verified steps, each one read back. Then in production, on your word, with a backup taken first.
The records pass, the forms post, the integrations round-trip. The record of every run stays with you.
Our own CRM rebuild, October 2026, built through code in a sandbox and read back after every step.
Named by category, because a platform is a platform: the method is the same wherever the configuration can be written as data and read back through an API.
Modules, fields, picklists, layouts, assignment and stage rules, and the CRM integration services to everything that touches a customer.
Items, price lists, tax and the posting rules, connected to the CRM and the inventory so an invoice agrees with what shipped.
Departments, ticket fields, service levels and the customer record shared with the CRM, so an agent knows what the customer bought.
Lists, scoring, the nurture journeys and the CRM handoff, so a lead never waits for someone to notice it.
Warehouses, assemblies, channels and the 3PL feed in one count that the invoice agrees with.
Forms that post to the CRM and the marketing automation platform, with every submission read back.
Reporting and storefronts have pages of their own: business intelligence services and eCommerce services.
An AI readiness assessment is part of discovery: five questions decide how much of the build the AI can do on the first pass and what has to be settled first.
Each customer, order, item and invoice has one owner; every other system reads it.
A stage, a status or a type has one definition, shared by every module that uses it.
Someone is named for each record type and answers for its quality.
Each one has a direction, a trigger and a failure behaviour on paper, not in one person’s memory.
A place where the plan runs and is read back before production sees it.
Fewer than five is the usual starting point. Discovery says which are missing, and the model is where they get written down.
A system configured this way is a system an agent can read: one record per fact, picklists with one meaning, rules written as data. That is the ground an agentic one-surface solution stands on, where an agent reads across your applications, proposes the next step and acts, with you approving the outcomes.
A short conversation about which system goes first, what the model would hold and what the fixed price covers. We will tell you if we are a fit, and if we are not, we will tell you that too.
