Get Amp'd!

Data and AI onAWS
built to run the business

Agivant runs a dedicated AWS practice that builds the full data and AI stack on the cloud your data already sits on. It starts with one business challenge, gets the data behind it ready, and takes the answer all the way to agents working in production, with results in days and weeks.
Built on AWS, with Amazon Redshift, AWS Glue, Amazon S3, Amazon Kinesis, Amazon Neptune, Amazon SageMaker AI, Amazon Bedrock, and Bedrock AgentCore, Agivant engineers cost into the architecture before the first bill arrives.
Explore how you can build on AWS
Get Amp'd to move from cloud data to agents in production on AWS

Get Amp’d to move from cloud data
to agents in production on AWS

A data and AI stack earns its keep in production. Amp’d is how Agivant gets there. Bolt scaffolds the agent stack, Forward Deployed Engineers build inside the enterprise’s own AWS accounts, and production-grade agents reason over data the business already trusts. Each engagement ships with evaluation and rollback, and every hardened pipeline and agent becomes a reusable asset the next build starts from. Every build amplifies the next.

Why AWS carries the whole
data and AI stack

When a stack has to run the business, it helps that every piece was built to work with the next. Redshift and an S3 data lake hold the facts. Neptune holds how those facts connect. SageMaker AI and Bedrock turn them into predictions and answers, and AgentCore runs the agents that act on them. Fewer vendors in the chain means fewer places for things to break, and one bill to explain instead of five.
Why AWS carries a data and AI stack end to end

What Agivant brings to AWS

Graph from the first sprint

Graph from the first sprint

Amazon Neptune goes in early, so answers about fraud, suppliers, and customer networks carry the relationships behind them.
Analytics that runs beside the business

Analytics that runs beside
the business

Your production databases keep serving customers at full speed while Agivant builds the analytical layer alongside them.
One team from pipeline to agent

One team from
pipeline to agent

The engineers who write the Glue job also build the agent that relies on it, so nothing gets lost in a handover.
Cost engineered in from the start

Cost engineered in from
the start

Token budgets, cost tags, spend alerts, and right-sized compute go in with the first pipeline.
Production from day one

Production from
day one

Every agent ships observable, with its own identity and a person approving anything that carries real money or risk.
FinOps designed into AWS data and AI architecture

FinOps, designed in before the first bill arrives

Standing up AWS is the easy part. Keeping the bill predictable once agents call foundation models thousands of times a day takes design. Agivant bakes financial accountability, visibility, and cost efficiency into the architecture and into how teams work, so finance watches the cost of AI grow rather than discovering it after the fact.
Token budgets per agent and per use case, with prompt caching and batch inference on Amazon Bedrock to cut repeat spend
The lightest Bedrock option that answers well, with larger ones kept for the questions that need them
Every workload tagged to an owner, with AWS Budgets and Cost Explorer showing spend by team and use case
Storage tiers and Savings Plans matched to how each workload runs, with Redshift Serverless scaling down when queries stop

Reported outcomes

up to 70%

lower token spend and up to 35% lower monthly cloud bill

Put four connected layers to work, from data to agents

Each layer on AWS makes the next one work better. Clean data sharpens predictions, predictions ground answers, and answers people trust give agents something solid to act on.
Get the data ready, and keep it fresh.
Agivant pulls data out of your transactional systems with AWS Glue, lands it in an Amazon S3 data lake, and shapes it into tables Amazon Redshift can query fast. With streaming through Amazon Kinesis, dashboards run minutes behind the business. Amazon Neptune maps how customers, accounts, suppliers, and assets connect, so the next question already has context.
Built with: Amazon Redshift · AWS Glue · Amazon S3 · Amazon Kinesis · Amazon Neptune
Predict what happens next.
Much of the value in enterprise AI still comes from good predictive data science. Agivant builds forecasting, churn, pricing, and anomaly detection on Amazon SageMaker AI, and retrains it automatically as new data lands. Analysts can call predictions straight from SQL with Amazon Redshift ML.
Built with: Amazon SageMaker AI · SageMaker Pipelines · SageMaker Feature Store · Amazon Redshift ML
Ask in plain English, and see what changed and why.
Assistants on Amazon Bedrock answer the questions your business actually asks: where sales dipped, which customers are drifting, which anomaly needs attention this week, which events to watch next. A semantic layer of business definitions sits underneath, so recognized revenue and gross margin mean exactly what your finance team means by them. Every answer shows its source and its definition, because an answer nobody can explain is hard to act on.
Built with: Amazon Bedrock · Bedrock Knowledge Bases · Bedrock Guardrails · Amazon Neptune Analytics
Agents recommend the next best action, then follow through.
Once an answer is clear, the agent lays out the next best action and what each option is likely to change. Your people pick the call, and agents carry it out: raise the ticket, reconcile the invoice, tell the customer, update the forecast. Agivant builds and runs them on Amazon Bedrock AgentCore, which gives every agent an identity, memory, a gateway to enterprise tools, and full observability.
Built with: Amazon Bedrock AgentCore · Amazon Bedrock Agents · AWS Lambda · AWS Step Functions

Keep teams in control while agents
run the work

Every Agivant agent is production grade from day one, observable and always on. Each one carries its own identity in AWS, and a person approves anything that moves real money or carries real risk. Routine steps run on their own, and the calls that need judgment route to the team that owns them.
Keep teams in control while agents run the work
Scale on AWS with Well-Architected engineering

Scale on AWS with Well-
Architected engineering

Agivant builds inside your own AWS accounts, so data and agents sit on the infrastructure the enterprise already runs. Every build follows the AWS Well-Architected Framework across all six pillars, and Forward Deployed Engineers work in focused sprints. This is what an Amp’d enterprise looks like in production.

Industry solutions on AWS

Fraud and exposure
Spot fraud rings and risky exposure while there is still time to act.

Key capabilities

  • Account, device, payee, and address links mapped in Amazon Neptune
  • Risk scored by SageMaker AI as transactions land
  • Cases opened for investigators with the evidence attached
  • Investigator review before any account action

Built with

Amazon Neptune · Amazon Redshift · Amazon SageMaker AI · Amazon Bedrock AgentCore

Reported outcomes

up to 60%

faster fraud ring detection and up to 30% fewer false positives

How An Engagement Runs

Put AWS to work on one business challenge

Something your team owns, with a number you want moved.

Map it to the stack

Agivant traces the data behind the challenge and designs the AWS architecture that answers it, with the running cost worked out up front.

Put it in production

Forward Deployed Engineers build inside your own AWS accounts, with results in days and weeks.

Build the next one faster

Every pipeline and agent becomes a reusable asset the next build starts from.