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When OpenAI recently announced a $10 million minimum price tag for its custom AI consulting service, it wasn’t just signaling a new revenue stream. It was quietly admitting something we have known from the beginning: deploying advanced, agentic AI into real-world enterprise workflows is not a tooling problem; it’s a people, knowledge, and trust problem.
The market is flooded with modular frameworks, sandbox tools, and AI “copilots” promising plug-and-play automation. But the hard truth is this: regulated industries don’t need another connector, they need confidence. They need agents that actually do the work, are audit-ready from day one, and improve every month, not just every model release.
We’ve been delivering exactly that since day one. Not for $10 million, but for real budgets. Not with demos, but with deployed agents closing loans, underwriting policies, and adjudicating claims. And not just with software, but with a full-service partnership designed to extract knowledge, reduce risk, and make AI real.
Our Full-Service Partnership Model
Our deployment model isn't just about delivering a tool, it's about delivering outcomes.
Our forward deployed engineers (FDEs) embed with business and technical teams to deeply understand the nuance of each workflow. They partner with SMEs (subject matter experts), configure AI agents inside the AgentFlow platform, and continuously improve them based on feedback.
This isn’t a side service. It’s core to how we make agentic AI work for finance and insurance. While others sell sandboxes, we bring shovels and get to work.
What follows is a walk-through each stage of our partnership process, from first contact to agent handoff.
Step Zero: Identifying Your Agentic AI Use Case
We don’t assume you have a fully baked roadmap. In fact, many of our customers start with a question: Where should we begin?
This is where our work starts, not ends. Before a single line of code is written, our team runs discovery sessions with operational leads to:
Understand current pain points, handoffs, and failure modes
Evaluate existing tools and where they fall short
Recommend a starting point for your first agentic deployment
This pre-work ensures that the initial use case is not just viable, yet it’s visible across the org.
Discovery and Scoping
Once a use case is agreed upon, our forward deployed team begins deep immersion with your domain experts. This isn’t a surface-level requirements gathering exercise. We sit down with underwriters, claims analysts, loan officers, and operations leads to map out their day-to-day. We walk through actual files, decision logs, SOPs, and edge-case playbooks. We learn the exceptions as much as the rules.
We also audit the digital ecosystem: what systems are involved, which databases are upstream, and what decisions flow into downstream tools. These sessions result in a shared blueprint that defines the inputs an agent needs, the logic it must apply, and the way outputs must be structured to plug seamlessly into your operational architecture.
Knowledge Extraction
Enterprise workflows don’t run on clean data alone. They run on tribal knowledge, the mental models built up over years of experience. That’s what we extract next.
We use structured interviews and scenario-based whiteboarding sessions to capture how experts make judgment calls. How does an experienced underwriter flag borderline cases? When does a claims adjuster escalate a field report? We translate these instincts into structured decision logic, prompt strategies, and escalation rules that live inside your agent.
By the end of this phase, the agent doesn't just "know the workflow", it thinks like your best employees.
Enterprise Intelligence Integration
Next, we plug the agent into your world.
That means ingesting structured data from systems like Salesforce, Workday, or internal databases, and layering in unstructured content: policy manuals, past decisions, email threads, and PDF documents. We unify this enterprise intelligence, tag it with semantic context, and align it with each step of the workflow.
This isn’t just integration. It’s the groundwork for true agentic reasoning, where the AI doesn’t just process data, but understands how it maps to real decisions.
AI Agent Calibration
At this stage, the agent comes to life.
We fine-tune the model (closed-source or open) with domain-specific logic, integrate confidence scoring, and simulate interactions that mirror live environments. Each decision is explainable. Each action is bounded by pre-defined rules and thresholds.
We run iterative QA cycles with SMEs, walking through real scenarios to confirm the agent acts as expected, not just statistically, but contextually.
Validation and Testing
This is where most demos stop. We go further.
Our validation process uses synthetic and historical data to ensure real-world readiness. We conduct traceability reviews with your business and compliance teams, vet edge-case handling, and ensure decisions are justifiable under scrutiny.
Nothing goes live until domain owners sign off.
Deployment and Handoff
When agents are ready, we deploy them within your VPC or on-prem environment using AgentFlow’s API-first, secure architecture. Monitoring happens via specialized dashboards: AgentFlow Monitor for IT, Command for executives, and Review for team leads.
But deployment isn’t the end. We train transformation leads and business teams to monitor agent behavior, review audit logs, and provide feedback through a no-code interface.
Ownership transitions to your team, but we stay close. We remain available for updates, expansions, and performance tuning.
Deploy AI With Our Easy Full-Service Partnership Model
The future of work in finance and insurance doesn’t depend on tools, but it depends on trust, transfer, and transformation.
Our full-service partnership model delivers all three.
And unlike the $10 million "consulting" retainer OpenAI just introduced, ours comes with actual deployment, institutional knowledge capture, and measurable business outcomes.
If you're ready to stop experimenting and start deploying, please book a call. Our experts can show you how AgentFlow can make AI deployment effortless.
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