AI agents
Agents that take on a defined piece of work, on Agentforce or another platform, with limits and a point where a person takes over.
AI solutions
AI can take on real work. The harder questions are which work, under whose authority, and how you will know it paid off. We answer those with you, then we design and build the solution.
What we build
Agentforce is one way to put AI to work on Salesforce. It is not the only one. We choose by the job.
Custom MCP servers
One governed route that lets your AI assistants read and act on Salesforce data, under the permissions you set.
AI assistants
Assistants that help your people research, draft and decide, inside the tools they already use.
Automations and integrations
The routine steps between systems, redesigned and automated so nobody has to chase them.
Where AI works
Each function has different work, different risks and different measures. The discipline is the same: start from the number, then decide what the AI does.
AI for Sales
We design and build AI agents and assistants that give sellers time back and keep the pipeline accurate, with the value measured.
ExploreAI for Service
We design and build AI agents and assistants that resolve routine contacts, support advisers and protect service quality.
ExploreAI for Operations
We design and build AI agents, automations and integrations that clear exceptions and approvals faster, inside your controls.
Explore
AI orchestration
Every use case we build follows the same five-step shape. If a step is missing, the use case is not ready.
- 01
Signal
A customer, a record or an event asks for attention.
- 02
Reasoning
The AI reads the context and plans a response.
- 03
Action
It acts within its permissions.
- 04
Human approval
A person approves what crosses a set threshold.
- 05
Outcome
The result is recorded against a business measure.
A generic pattern. Thresholds and approval rules are set for each use case.
Architecture
AI is most useful, and easiest to govern, when it works with the platform that already holds your customer data, your permissions and your processes. We design for that, layer by layer.
05
Experience
Where customers and employees meet the AI.
- Web and mobile
- Slack
- Email and messaging
- AI assistants
04
AI agents and tools
Each with a defined role, scope and set of actions.
- Agentforce agents
- Custom MCP servers
- Assistants
- Automations
03
Trust and guardrails
The rules every agent and assistant works within.
- Permissions
- Approval thresholds
- Audit trail
- Policy checks
02
Data (Data 360)
Unified, governed context the AI can rely on.
- Customer profiles
- Consent
- Knowledge
- Activity history
01
Systems of record
The applications where work is recorded.
- Agentforce Sales
- Agentforce Service
- ERP via MuleSoft
- Line-of-business systems
Governance and trust
An AI agent should gain freedom the way a new colleague does: step by step, with evidence. We build the controls that make that possible.
Guardrails
What each agent may and may not do, written down before it goes live. Scope, limits and escalation paths that a non-technical leader can read.
Auditability
Every action traceable to a reason and a record. If you cannot explain what an agent did, it should not have been allowed to do it.
Data privacy
AI sees only the data its task needs. Consent and access rules are treated as design constraints from the first workshop.
Human oversight
Approve before, review after, or handle exceptions only. The oversight model is chosen by risk, and revisited as evidence builds.
Responsible AI policy
We help you write a short, usable policy that sets out how your organisation will use AI, who is accountable, and how concerns are raised. It is a working document, not a compliance exhibit.
Outcomes
We agree the measures before the work starts and set a baseline for each one.
We do not publish headline numbers. Targets are set with each client against their own baseline.
Time to production
How long it takes to move from a shortlisted use case to AI doing real work.
Case deflection rate
The share of service contacts resolved without a person, at a held or improved satisfaction level.
Forecast accuracy
How close the forecast lands to the result, quarter after quarter.
Selling time
The share of a seller’s week spent with customers instead of on administration.
Cost to serve
What it costs to handle a customer request from start to finish.
Human override rate
How often people correct or reverse the AI. A measure of trust, tracked over time.
Readiness
Most are ready in some places and not in others. Our readiness assessment scores data, process, people and governance, and tells you where to start.
- A scored view across four dimensions
- The gaps that would stall a first agent
- A short, ordered list of what to fix first
- Data
- Process
- People
- Governance
A first conversation costs nothing and usually clarifies a great deal.