AI Integration & Acceleration
Getting a model into production is only the beginning. The real challenge is turning AI into a capability that is reliable, secure, observable and connected to the business.
We work end to end on the architecture, integration and operation of AI workloads, connecting models to the systems, data and processes a company already runs.
Outcome
AI running inside the product or the operation, with data, cost and model behavior under control.
What we do
Integration into the product and the operation
We connect models to what already exists, through the APIs and data paths the company runs today, without requiring the product to be rewritten around the AI.
Infrastructure architecture for AI
We design network, identity, data isolation and capacity for AI workloads on AWS, using Amazon Bedrock or model APIs such as Anthropic Claude, whichever the case calls for.
Context and tools for the model
We give the model access to what it needs: retrieval over the company's own data, tool use and MCP to reach internal systems without copying the database.
Cost and behavior under measurement
Cost per use case, latency and answer quality measured from day one. AI without measurement becomes spend with no owner.
Evaluation before production
We agree with the team on what counts as a correct answer and measure against it, using a test set built from cases in the domain itself.
How we work
We scope the use case
The choice of model follows from the problem, the data available and the cost per answer the business can accept.
We connect to real data early
A demo on sample data hides the work that matters: permissions, data quality and latency only show up once the real system is in.
We measure cost and quality before scaling
A use case that costs more than it returns is new debt, even when it works.
We ship with a way back
Gradual rollout, spend limits, request logging and the ability to switch the integration off without taking the product down.
We leave the team ready to operate
Documentation and joint review leave the team able to switch models, tune the context it feeds them and widen the use case by reading its own cost, latency and quality metrics.
AWS services and tools involved
- Amazon Bedrock
- Anthropic Claude
- MCP
- AWS Lambda
- Amazon ECS
- Amazon CloudWatch
- Terraform
Where we usually come in
The proof of concept worked, then stalled
Companies with a pilot that convinced people internally and now needs integration, access control and operations to become part of the product.
The model needs the company's data
Cases where the answer is only worth having with proprietary context, and the question becomes permissions, data quality and access paths.
Nobody knows what the AI costs
Teams already running models in production without measuring cost per use case, who need that before widening usage.