AI & Data Practice
Agents that finish the work — and show their working
An assistant suggests; an agent acts. We build agents that carry a task across your CRM, databases and APIs from trigger to completion — pausing for human approval at every step you deem consequential, and logging each decision so oversight is a query, not an act of faith.
- Approval gates on consequential steps
- Every action logged and replayable
- Scoped credentials, revocable anytime
The operational reality
Why cross-system work still eats your best people's week
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Every case means five tabs and a spreadsheet
Resolving one customer issue touches the CRM, an order database, a carrier portal and email. The knowledge lives in people's heads, so throughput scales only with hiring.
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Chatbots talk; nobody acts
First-generation AI answered questions and then handed the actual work back to a human. The queue never got shorter — it just got a friendlier front door.
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Full autonomy is a non-starter for risk
Letting a model issue refunds or edit records unsupervised fails any sensible review. Without a designed oversight layer, the whole initiative gets shelved as too dangerous.
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Scripted automation shatters on exceptions
RPA bots replay keystrokes until the form changes or the case gets unusual. The exceptions flow back to humans — and exceptions were most of the cost to begin with.
The shift
From answering machine to accountable operator
The difference between a chat assistant and a production agent is everything wrapped around the model.
- Suggests steps; a person still executes them
- Sees only what gets pasted into the chat
- No memory of the case once the window closes
- No record of why it recommended anything
- Risk managed by hoping users stay sensible
- Executes the workflow end to end through scoped tools
- Reads live context from CRM, database and API
- Tracks case state across every step and retry
- Writes a replayable audit record for each run
- Risk managed by approval gates you configure
What we build
The agent, and everything that makes it safe to run
Process Selection
Scoring candidate workflows by volume, reversibility and value, so the first agent earns its keep fast.
Agent Design & Build
Planning logic, tool definitions and retry behaviour engineered for your specific process, not a template.
System Integrations
Scoped, credentialed connections into Salesforce, databases, ticketing and internal APIs.
Oversight Layer
Approval gates, escalation queues and kill switches classified step by step with your process owners.
Simulation & Testing
Agents rehearse on historical cases in a sandbox before they touch a live record.
Run Operations
Dashboards, alerting and audit reporting for the team that supervises the agent fleet.
How we work
Autonomy is granted, never assumed
Discover
Map the process & its exceptions
Assess
Classify actions by reversibility
Design
Tools, gates & audit schema
Implement
Build, sandbox & shadow-run
Optimize
Widen autonomy as trust accrues
Manage
Supervised operations, 24/7 option
Why it matters
The case your COO will want to see
Statements reflect engagement objectives; actual gains depend on process volume and complexity, measured against baselines set in discovery.
Under the hood
The stack behind a production agent
Industry applications
Where agents already carry real workloads
- Customer SupportTriage & resolution
- E-commerceOrder & returns handling
- FinanceKYC & document checks
- LogisticsShipment exceptions
- HealthcareIntake & scheduling
- SaaSOnboarding & ops tasks
Related
Natural next steps
Questions CIOs ask
The autonomy conversation, answered straight
What stops an agent from hallucinating its way into a bad action?
An agent can only do what its tools permit. We scope each tool to the narrowest useful action, validate parameters before execution, and route anything consequential — payments, record deletion, external communication — through an approval gate. A wrong idea inside the model becomes a blocked request, not a damaged record.
Where does the human stay in the loop?
Wherever the action is hard to reverse. During design we classify every step as auto-approve, notify-after, or hold-for-approval, agreed with your process owners. Early in an agent's life most consequential steps hold for approval; thresholds relax only as its decision history earns confidence, and always under your control.
Can we audit what an agent did and why?
Yes — that is a build requirement, not an add-on. Every run records the triggering input, the reasoning summary, each tool call with parameters and results, and who approved what. The trail is queryable, exportable and retained to your compliance schedule, so an auditor can reconstruct any decision.
How do agents connect to systems we already run?
Through the same APIs and service accounts your integrations use today — Salesforce, HubSpot, SQL and NoSQL databases, internal REST services, ticketing platforms. Agents receive their own scoped credentials, so access can be reviewed, limited and revoked exactly like any other system identity.
Which process makes a sensible first agent?
One that is frequent, rule-describable and annoying — order status investigations, data enrichment, ticket triage, follow-up chasing. High volume proves value quickly; low blast radius keeps risk manageable while your organization learns to operate agents.
Nominate the workflow your team dreads most
Bring one repetitive, multi-system process to a working session. We will map it, classify its risks and tell you honestly whether an agent belongs there yet.