Agentic AI
Agentic AI that works inside your operations, with people in control.
We design, build and run AI agents that read, decide within guardrails, act in your systems and hand over to people at the right moment.
An illustrative claim journey in which AI agents handle the volume and people make the decisions:
- Inbound call (input): Call answered · AI disclosed.
- Voice intake agent (AI agent): Policy verified · case opened.
- Case manager (a person decides): GOP approved.
- Provider follow-up agent (AI agent): Invoice & report received.
- Claims intake agent (AI agent): Line items extracted.
- Bill-review agent (AI agent): Flags explained.
- Duplicate check (AI agent): No duplicate found.
- Adjudicator (a person decides): Claim approved.
- Customer update agent (AI agent): Customer informed.
Our point of view
How we think about AI agents
Start from the outcome
We begin with one operational workflow and the result it needs, then choose the technology.
People stay in control
Every agent has clear points where a person reviews, approves or takes over, and every action is logged.
Built into your systems
Agents read and write where your teams already work: policy, case, claims, telephony and finance systems.
What we build
The agents we build
Most workflows use more than one. For example, a voice agent opens the case, an intake agent structures the documents and a decision-support agent prepares the evidence.
Voice agents
Inbound and outbound calls for first notice, provider follow-ups and customer updates, with warm handoff to a person.
Document & intake agents
Read, translate and structure invoices, reports and forms in many languages.
Decision-support agents
Apply your rules and propose a decision with the evidence behind it.
Back-office workflow agents
Chase, reconcile, route and update across systems, inside limits you set.
Knowledge agents
Answer questions from policy wordings, SOPs and manuals, with references to the source.
Custom AI platforms
The portals, consoles and dashboards where people and agents work together.
How we build
How we build an agent
We follow the same steps for every agent, whether it ends as a pilot, a product or a managed service.
- 01
Discover
We shadow your team and pick one workflow with a clear, measurable outcome.
- Workflow map
- Success criteria
- Data & system inventory
- 02
Design
Agent roles, tools, autonomy level and the exact points where people decide.
- Agent specification
- Escalation thresholds
- Human checkpoints
- 03
Guardrails
Least-privilege tool access, PII redaction and protection against prompt injection, since claim documents and calls come from outside your organisation.
- Threat model
- Access policy
- Disclosure scripts
- 04
Integrate
Connect to your core systems through APIs, with automation fallbacks where APIs do not exist.
- Connectors
- Audit logging
- Test environment
- 05
Evaluate
Test against your own anonymised history, run in shadow mode next to your team, and red-team before launch.
- Evaluation set
- Shadow-mode report
- Go-live decision
- 06
Operate
Monitoring, drift detection and review queues. Our team can staff these for you.
- Monitoring
- QA sampling
- Continuous improvement
Autonomy, earned
Four levels of autonomy
Each action an agent takes has a set autonomy level. Select a level to see what it means in practice.
What it means
The agent prepares a recommendation with its evidence. People decide.
Example
Adjudication support with clause citations.
Agents start at the lowest level that delivers value, and move up only with evidence from evaluation and QA.
Agent blueprints
Starting points for insurance operations.
Starting points that we adapt to your systems, rules and languages. Each one links to its stage in the claims journey.
- Voice first-notice agentAnswers the call, verifies the policy, records the details and hands over.See it in the journey
- GOP drafting agentDrafts guarantees of payment for a person to approve.See it in the journey
- Provider follow-up agentChases reports, estimates and invoices by phone.See it in the journey
- Multilingual intake agentTurns foreign-language invoices into structured claims.See it in the journey
- Bill-review agentLine-item flags and repricing worksheets for reviewers.See it in the journey
- Customer update agentProactive status updates in the customer’s language.See it in the journey
Guardrails
Where the line is
We agree these with you before any agent goes live and build them into the agent itself.
People always decide
- Medical decisions and fitness to fly
- Guarantee-of-payment approval
- Claim denials
- Fraud determinations
- Anything involving a vulnerable customer
Agents never
- Deny a claim on their own
- Make a medical judgement
- Act outside the limits you set
- Hide that they are an AI
Governance
Designed to stand up to audit.
Security, privacy and human oversight are built into each agent from the start.
ISO 27001 & 9001 lead-auditor led
Information security and quality practices led by a certified Lead Auditor, applied to every agent.
Privacy by design
Processes aligned with GDPR and UK GDPR, HIPAA for US health data, and India’s DPDP Act. PII is minimised and redacted wherever possible.
EU AI Act-aware
Each use case is risk-assessed, with human oversight, logging and AI disclosure designed in.
Data residency
Deployed in your cloud account and region, using in-region model endpoints where available. Your data is not used to train shared models.
Audit trail
Every agent action, input and decision is logged, so you can always answer “why did it do that?”.
Security testing
Prompt-injection defences, least-privilege tool access and red-teaming before launch.
Engagement models
Four ways to work with us.
Workflow assessment
We map one workflow and the data and systems involved, and show where an agent would help and where it would not.
Pilot
One agent, one workflow, real data in shadow mode, with go-live criteria agreed up front.
Build & integrate
Production agents connected to your systems, with monitoring and handover to your team.
Managed operations
We run the agents and the people behind them, as one accountable service.
Managed operations
Our operations teams can run the human side of every agent we deliver, around the clock.
FAQ
Questions about agentic AI.
What makes an agent “agentic”?
A chatbot answers questions. An agent completes a task: it reads inputs, decides what to do within rules you set, uses tools in your systems, and escalates to a person when it should.
Which AI models do you use?
We are model-agnostic and choose per task, weighing quality, cost, latency and where the data is allowed to go. Agents can be moved between models as the market changes.
How do you stop agents from making mistakes?
We combine evaluation against your own anonymised history, shadow mode before launch, explicit autonomy limits, human checkpoints and continuous QA sampling. Agents start at a low autonomy level and earn more only with evidence.
Can we start small?
Yes, and we recommend it. Most engagements begin with a workflow assessment or a pilot with a single agent.
Who owns what you build?
Ownership of deliverables is agreed in your contract. Your data stays yours and is not used to train shared models.
Next step
Start with a workflow assessment.
Sthiram · Efficiency with stability