Capabilities

What we deliver

From cloud infrastructure to the software that runs on it, IFT covers the full lifecycle of a federal system: design, build, secure, and operate.

01

Cloud architecture & infrastructure as code

We design and operate AWS environments for mission workloads, aligned with the AWS Well-Architected Framework. Every environment is defined in code (AWS CDK and CloudFormation), so development, test, and production are consistent, reviewable, and reproducible. We have worked alongside AWS Professional Services on infrastructure design for federal data platforms.

  • AWS CDK
  • CloudFormation
  • ECS & EKS
  • Lambda
  • RDS & DynamoDB
  • S3
  • IAM & KMS
  • CloudWatch
02

Secure cross-domain data systems

We build systems that move data between networks at different security levels: streaming replication that preserves strict transaction order, policy-driven bulk export with row-level security enforced at query time, and filtering and validation that support compliance review of every transfer.

  • Apache NiFi
  • Apache Kafka
  • Row-level security
  • Data filtering & validation
  • XSLT / XML schema
03

Software engineering

We develop the applications, microservices, and APIs that mission systems depend on, plus the tooling around them: code generators that remove hand-written boilerplate and simulators that allow full end-to-end testing in unclassified environments.

  • Java & Spring Boot
  • Python & Django / FastAPI
  • TypeScript & React
  • REST APIs
  • Microservices
04

DevSecOps & automation

We build CI/CD pipelines that compile, test, scan, containerize, and deploy across multiple environments, including pipelines purpose-built for classified deployment topologies. We also establish the configuration management standards that make releases reliable.

  • GitLab CI
  • GitHub Actions
  • Docker
  • Kubernetes & OpenShift
  • Vulnerability scanning
05

Data engineering & AI

We design data lakes and ingestion pipelines tuned to real workload patterns, and we build applied generative AI workflows, including retrieval-augmented generation (RAG) over an organization's own documents.

  • Data lakes
  • Geospatial metadata
  • PostgreSQL & pgvector
  • RAG pipelines
  • LLM integration

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