GenAI Solutions

Move GenAI From Pilot to Production.

Secure, governed GenAI applications on Claude via Amazon Bedrock, including enterprise chatbots, RAG systems, document intelligence, and product copilots.

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CHALLENGES

The GenAI demo is easy.
Production is where most programs fail.

Common blockers include 

Pilots that never scale

Prototype architectures fail under real data, users, and security reviews.

Low trust in answers

Unsourced or inconsistent outputs limit adoption.

Security concerns

Legal and compliance teams need clarity on data access, audit trails, and model usage.

Knowledge trapped in documents

Critical information sits across policies, contracts, manuals, videos, tickets, and runbooks.

Disconnected AI experiences

Standalone chatbots create limited value unless embedded into workflows and products.

SOLUTION

SourceFuse builds GenAI systems designed for enterprise adoption and production use.

We deliver 

Enterprise knowledge assistants

AI copilots that answer from governed internal knowledge with source references.

Customer-facing chatbots

Secure RAG-powered assistants for portals, products, and support experiences.

Document intelligence

AI for compliance review, contract analysis, policy interpretation, and structured extraction.

RAG pipelines

Hybrid search, vector databases, embeddings, retrieval governance, and answer traceability.

Claude on Amazon Bedrock

Private, IAM-aligned, auditable deployment inside the client’s AWS environment.

GenAI governance

Guardrails, access control, prompt management, PII controls, human review, and audit logs.

Business Impact

Higher production success

Architecture built for scale, security, and adoption from day one.

Faster knowledge access

Employees and customers get sourced answers instantly.

Reduced manual review

Accelerate document-heavy workflows across legal, compliance, support, and operations.

Improved trust

Source attribution and governance reduce hallucination risk.

Security review readiness

Bedrock, IAM, guardrails, and audit trails address enterprise AI concerns.

customer stories

From fragmented data to measurable outcomes.

Financial Services / Payment Processing

Situation

A fintech serving banks and credit unions had institutional knowledge trapped in SOPs, recordings, and static docs, slowing onboarding and overloading SMEs.

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Outcome

  • Built Genie, a GenAI knowledge assistant on Amazon Q Business with natural-language search
  • Faster access to SOPs, meeting summaries, and internal docs via self-service queries
  • Shorter onboarding, new hires query Genie instead of relying on peers or SMEs
  • Broke down knowledge silos by centralizing searchable, RBAC-governed content
  • Minimal operational overhead via a fully serverless AWS architecture
Financial Services / Payment Processing
Energy Sector - Well Engineering

Situation

An early-stage well-engineering startup needed to replace manual, siloed documentation with intelligent, scalable, compliance-ready AI systems.

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Outcome

  • Delivered ELLIS, an AI engineering SME (powered by Claude Sonnet 4) for early risk detection and contextual decisions
  • Built WellPhase, a platform automating engineering deliverables with RBAC and tenant management
  • 8-week Discovery phase cut time-to-market and de-risked AI adoption
  • Compliance-first, cloud-native architecture with role-based access and encryption
  • Cost savings via optimal AI model selection; intuitive Figma-validated UI/UX eased onboarding
Energy Sector - Well Engineering