Case study · SaaS · AI on AWS
iSEEit Raised MEDDPICC Qualification Coverage from 42% to 85% with Agentic AI
iSEEit builds sales execution and opportunity management software that puts the MEDDIC and MEDDPICC qualification methodology inside the seller's daily workflow. Its customers are B2B revenue teams who depend on consistent deal qualification, accurate forecasting and repeatable coaching.
To take manual data entry off sellers and make qualification continuous rather than periodic, iSEEit engaged HabileLabs to design and deliver a secure, governed, multi-agent AI environment on AWS.
- 42% → 85% MEDDPICC coverage
- About 3 hours saved per seller per week
- 100% of agent actions logged
Results at a glance
18%
Better forecast accuracy as qualification data became continuous and evidence-linked
92%
Agreement between agent MEDDPICC assessments and expert human review
47%
More weekly active usage of qualification features after launch
32%
Lower inference cost per processed deal
The Challenge
The agents had to interpret unstructured sales signals, reason over them against the MEDDPICC framework and take auditable actions, such as updating qualification fields, surfacing deal risk and recommending the next best action, while keeping strict tenant isolation and enterprise data protection.
01
Manual Qualification Burden
Sellers had to maintain structured MEDDPICC data by hand, which led to incomplete records, stale deal information and low methodology adoption.
02
Unstructured Signal Overload
Qualification evidence was scattered across call transcripts, emails and meeting notes, in formats no rules-based system could reliably interpret.
03
Multi-Step Reasoning
Deciding whether an economic buyer has been identified or a decision process validated requires reasoning over evidence, not single-shot text generation.
04
Accuracy and Hallucination Risk
Any AI-generated change to a customer's CRM system of record had to be grounded in evidence, explainable and auditable.
05
Data Isolation and Security
As a multi-tenant SaaS provider handling commercially sensitive pipeline data, iSEEit needed tenant-level isolation, encryption and assurance that customer data is not used for model training.
06
Governance of Autonomous Agents
Agents that act, rather than only advise, needed observability, evaluation, versioning and guardrails to run safely.
The Solution
HabileLabs designed iSEEit's agentic workloads on Amazon Bedrock and took them into production, using a supervisor-and-specialist agent architecture with Anthropic Claude models as the reasoning engine.
Multi-Agent Orchestration and Reasoning
Qualification workloads run on Amazon Bedrock with Anthropic Claude models, routed by task: a higher-capability model for multi-step qualification reasoning and a faster model for extraction and classification. A supervisor agent delegates to specialist agents for signal extraction, MEDDPICC qualification, deal risk detection and next-best-action generation. Amazon Bedrock Agents and AWS Step Functions coordinate them as stateful, retryable workflows. AWS Lambda exposes agent tools as discrete, least-privilege action groups, so every agent capability is explicit and permissioned on its own.
Grounding and Evidence-Based Retrieval
Amazon Bedrock Knowledge Bases provide retrieval-augmented generation over each tenant's deal history and playbooks, with Amazon OpenSearch Serverless as the vector store and Amazon Titan Text Embeddings generating the embeddings. Amazon S3 stores transcripts, documents and notes under tenant-scoped prefixes with server-side encryption. This immutable evidence layer lets every generated value be cited back to its source.
Safety, Guardrails, and Human Oversight
Amazon Bedrock Guardrails enforce denied topics, PII detection and redaction, and contextual grounding checks, blocking unsupported outputs before they reach a user or a CRM field. Autonomy is tiered: analysis and recommendations run automatically, while write-back to the customer's CRM requires human approval or a tenant-configured confidence threshold. Amazon DynamoDB holds agent state, action history and an approval ledger, so each tenant has a complete record of what was proposed, what was accepted and by whom.
Compliance and Security Management
AWS IAM session policies and per-tenant role assumption enforce isolation on every request, so no agent invocation can reach data outside its tenant. AWS KMS and AWS Secrets Manager protect keys and credentials and automate rotation, with encryption in transit and at rest. AWS CloudTrail and AWS Config provide continuous auditing and traceability.
Evaluation and Observability
A golden-dataset evaluation harness scores agent outputs on qualification accuracy, grounding and action appropriateness before any prompt, model or tool change is promoted to production. Amazon CloudWatch, Amazon Bedrock model invocation logging and AWS X-Ray capture per-step traces, token consumption, latency and guardrail interventions across the orchestration graph.
Event-Driven Processing and Integration
Amazon EventBridge and Amazon SQS start agent workflows as soon as a new transcript, email or CRM update arrives, so qualification runs continuously instead of as a periodic batch. Amazon ECS on AWS Fargate and Amazon API Gateway host and secure the application and integration services, scaling on demand with no capacity to manage.
Cost and Performance Optimization
Model routing, prompt caching and context compaction send each task to the smallest sufficient model and cut repeated token spend across multi-step reasoning chains. Token-budget ceilings per agent run and per-tenant cost attribution keep unit economics predictable as deal and transcript volumes grow.
Business Impact
42% → 85%
Qualification Coverage
Opportunities with complete MEDDPICC data increased from 42% to 85% within 6 months.
~3 hrs/week
Seller Productivity
Automated signal extraction and CRM updates removed approximately 3 hours per seller per week of manual data entry.
18%
Forecast Accuracy
Improved forecast accuracy by 18% as qualification data became continuous and evidence-linked.
92%
Qualification Accuracy
Agent-generated MEDDPICC assessments achieved 92% agreement with expert human review on the evaluation dataset.
47%
Adoption
Weekly active usage of qualification features increased by 47% following launch.
32%
Cost Optimization
Model routing, prompt caching, and token budgeting reduced inference cost per processed deal by 32%.
100%
Governance
100% of autonomous actions were logged with source citations and a complete approval trail.
Why iSEEit Chose HabileLabs
Managed Foundation Models
Amazon Bedrock provides frontier reasoning capability through a single secure API, with no GPU infrastructure to operate.
Enterprise Data Protection
Tenant isolation, encryption and assurance that customer data is not used to train foundation models.
Guardrails and Grounding as Core Controls
These make autonomous action safe enough to trust against a customer's system of record.
Agentic Architecture Expertise
Supervisor-and-specialist design, tool permissioning, evaluation harnesses and human-in-the-loop controls.
Predictable Unit Economics
Model routing, caching and token governance aligned to a SaaS pricing model.
End-to-End Delivery
From use-case definition and architecture through implementation, evaluation and steady-state operations.
Conclusion
Planning something similar?
With an agentic AI architecture on AWS, iSEEit turned sales qualification from a manual, self-reported exercise into a continuous, evidence-grounded system. HabileLabs built it on Amazon Bedrock with multi-agent reasoning, guardrails and human-in-the-loop governance for safe autonomous action. Qualification coverage, forecast reliability and seller productivity all improved, and the platform scales securely across a growing multi-tenant customer base.