Building the last mile of Enterprise AI.

Most AI projects stall at the chat interface. We implement the underlying architecture — connecting Amazon Bedrock to your document knowledge and business actions with absolute governance.

Discuss a pilot Review the reference implementation →

01. Structured Insight

Metrics & operational data prepared for reliable answers.

02. Document Context

Answers sourced from policies, SOPs, and knowledge.

03. Governed Action

Tasks, routing, and controls around who can do what.

Core Services

Operational AI on AWS

From document intelligence to governed automation, we build systems meant to be used inside real operating processes.

Document intelligence and RAG

For teams that need accurate, sourced answers from internal documents, playbooks, and business knowledge.

Document ingestion and curation
Answer accuracy and source attribution
Evaluation and response quality checks

Workflow automation

For teams that want AI systems to trigger or coordinate work, with clear controls over tools, actions, approvals, and fallbacks.

Amazon Quick Suite implementations

For teams that want analytics, policy-backed answers, and action paths inside a single operational workflow on AWS.

Operations and finance workflows
QuickSight-ready data modeling
Role-aware actions and routing

Reference Implementation

Battle-tested in-house.

We engineered this finance operations workflow end-to-end to validate the architecture before offering it to our clients. It serves as a production-grade benchmark for how data, policy, and action intersect in a single operating model.

100% Source Attribution

< 3s Inference Latency

The Challenge

Resolving financial exceptions often means piecing together information from multiple systems. Finance teams must review ERP records alongside vendor agreements and internal policies, creating a manual process that is slow, repetitive, and difficult to scale.

The Solution

To streamline that work, we built a policy-aware AI system that combines structured financial data with agreement intelligence. Using RAG on Amazon Bedrock, the solution retrieves the right contract language, applies internal credit policy, and recommends a governed next step for finance teams.

Technology Stack

Reasoning: Claude 3.5 on Amazon Bedrock
Knowledge: Amazon S3 & Vector Retrieval layer
Interface: Amazon Quick Suite
Execution: AWS Lambda for governed actions

ERP Layer Financial ERP data

↓

Knowledge Layer Agreement & policy retrieval

↓

Reasoning Layer AI-guided recommendation

↓

Action Layer Governed action routing

How we work

What a first project looks like

Choose one workflow

Pick one business process where data, documents, and actions already intersect. We focus on clarity over scale initially.

Model the inputs

Prepare the dataset, the document collection, and the action path before focusing on interface design.

Ship the governed path

Make the output operational, with role-aware controls and a clear end state. No theoretical models.

Define the next phase

After the first project, you will have a clear picture of whether to expand the workflow, integrate further, or leave it as-is.

Start small,
evaluate clearly.

If you already know the workflow you want to pilot, we can validate the scope. If not, we can help narrow it to one practical use case.

Book a 30-min call →

Direct Contact

[email protected]

Navigation