An agent is only as good as the data underneath it, which is why most AI projects are really data projects wearing a costume. We are straight about which is which before anyone signs anything.
An AI pilot that did not survive contact with reality
What it solves
Most AI projects fail on the data, not the model
The demo works because the demo data is clean. Yours is not, and no amount of model will fix a record nobody has updated since 2019.
Your data disagrees with itself
The same customer in four systems with three spellings and two owners. An agent given that will answer confidently and wrongly.
Nobody can say what good looks like
AI initiatives launched without a defined task, so success is a feeling and the pilot never graduates.
Trust has to be earned before it is delegated
Teams will not hand work to something they cannot check. Scope that ignores this produces a tool nobody uses.
Automation exposes broken process
An agent following a bad process does it faster. Most of the value is in the redesign, not the automation.
Governance is an afterthought
What the agent may see, may do, and must escalate — decided after go-live, which is too late.
The pilot cannot scale
A proof of concept built outside the platform, with no path to production and no owner.
Specialties
What we implement on Agentforce & Data Cloud
We do not start with the product. We start with the problem, then use only what solves it.
Readiness assessment
An honest read on whether your data and processes can support what you are being asked to deliver.
Data Cloud foundations
Unifying customer data across systems so there is one version of a person or an account to reason over.
Agent design and scoping
Choosing tasks narrow enough to succeed and valuable enough to matter.
Grounding and knowledge
Connecting agents to your own content so answers come from your business, not from a general model.
Guardrails and governance
What an agent may access, may act on, and must hand to a person.
Service agents
Deflection and routine resolution, with escalation paths people actually trust.
Sales agents
Qualification, follow-up and research on the parts of selling that do not need a person.
Measurement
Instrumenting from day one so the value can be shown rather than asserted.
Our process
You see working software long before go-live
The same shape on every engagement, sized to the business. Each stage produces something you can look at and react to, rather than a document you approve and hope for.
01
Discovery
We learn how your business runs before we touch the org — including the parts that are not on Salesforce yet.
02
Blueprint
Scope agreed and locked, priorities set, and a phased plan that says plainly what is not happening in phase one.
03
Build in phases
Regular demos of real, working configuration — so course corrections happen while they are still cheap.
04
Deploy and enable
Migration, testing, cutover and training owned by us — adoption is part of the job, not an afterthought.
05
Beyond go-live
The same team stays. Go-live is where the value starts compounding, not where the relationship ends.
Integrations
An agent is only as good as what it can reach
Most of this work is connective. These are the categories that determine whether an agent has anything useful to say.
Data warehouses
Bringing the company’s real data into reach without copying it everywhere.
Snowflake · BigQuery · Databricks · Redshift
Core business systems
Order, service and financial context an agent needs to answer properly.
ERP · Field Service · Service Cloud
Knowledge and content
Grounding answers in your documentation rather than in a general model.
Salesforce Knowledge · SharePoint · Confluence
Identity and permissions
Ensuring an agent sees exactly what the person it acts for is allowed to see.
Okta · Entra ID · Salesforce Shield
Channels
Where the agent meets the customer or the employee.
Web chat · Slack · WhatsApp · Email
Analytics
Watching what the agent actually does, and what it gets wrong.
Tableau · CRM Analytics · Data Cloud
Client stories
AI enablement in the wild
Commercial construction
McGough Construction
A 60-year-old contractor with a previous Salesforce implementation that had not taken hold. We rebuilt around usability first, then rolled out enterprise AI for knowledge work — with field teams identifying their own use cases rather than waiting on a mandate.
Salesforce CRM · Enterprise AI · Process standardisation
A unified Salesforce data architecture built first, which is what makes the AI layer viable now: an AI assist panel for support calls in testing, and Agentforce under active evaluation rather than assumed.
Service Cloud · AI Assist — in testing · Agentforce — evaluating
Not a cookie-cutter demo. A conversation about what you actually want automated and whether the foundations exist yet — including the answer that it does not, if that is the answer.
