What We Do
End-to-end software engineering, from strategy to production.
Integrated service lines, each governed by senior engineers and powered by the Agentic SDLC. Every engagement starts with the right team matched to your goals.
01
Intelligent Software Development
Custom software with AI agents embedded throughout the build from day one. We design systems that are agent-ready from the first architectural decision.
What we build
- •Web applications and enterprise platforms
- •Mobile apps (iOS, Android, cross-platform)
- •API platforms and integration layers
- •Agentic systems and AI-native workflows
- •Real-time data pipelines and dashboards
The Pod on every engagement
Every project is staffed with a fixed, senior, accountable team from kickoff through delivery.
- •Product Owner: translates business goals into a buildable roadmap
- •Lead Developer: owns architecture and daily engineering governance
- •Principal Developer: deep-domain expert, escalation authority
- •Lead SDET: quality engineering embedded from day one
Fixed team. Senior engineers. Full accountability.
02
Legacy Modernization
Understand your codebase. Build the roadmap. Modernize with confidence. We've modernized codebases in every language and maturity level, and we know how to move forward while preserving what already works.
Our approach
- •Technical debt assessment and scoring
- •Phased modernization roadmap
- •Strangler fig pattern or targeted rewrite
- •AI-readiness built into every phase
- •Zero-downtime cutover planning
The Legacy Modernization Agent
Before a single line is rewritten, our assessment agent maps the full landscape so you have complete visibility before work begins.
- •Automated codebase analysis and debt scoring
- •Dependency graph generation
- •Priority area identification and modernization sequencing
- •AI-readiness gap analysis
See the full picture before committing to a path.
03
Agentic Maintenance and Support
Production software needs more than a launch date. We use AI agents to run the maintenance layer: monitoring application health, triaging incidents, analyzing logs, surfacing regressions, and accelerating fixes with the same senior standards that built the system.
What we provide
- •24/7 production monitoring and incident response
- •Agent-assisted log analysis and root cause diagnosis
- •Automated regression detection and triage
- •Dependency, security patch, and release management
- •SLAs, escalation paths, and audit-ready reporting
The ADLC operations team
The same governance model that governs build engagements extends into keeping your production software healthy.
- •Continuous observability across applications, APIs, and infrastructure signals
- •Agent-assisted incident triage and remediation recommendations
- •Proactive detection of performance degradation and failure patterns
- •Lessons feed back into runbooks, monitoring, and future delivery
Production software stays healthy, observable, and supported.
04
AI & Agentic Systems
Research-first agent design and deployment. We evaluate each use case carefully, prototype quickly, and build what delivers measurable ROI in your environment.
What we do
- •Feasibility research and ROI assessment
- •Proof-of-concept design and validation
- •Multi-agent orchestration architecture
- •Knowledge bases and retrieval systems
- •Guardrails, governance, and audit trails
- •Enterprise deployment and rollout
Our approach
Every AI project starts with architecture-first planning and the right specialists in the room before any build begins.
- Product Owner — validates the use case against real business value
- Lead Developer — assesses technical feasibility and integration requirements
- Architect — reviews every use case for structural fit before we write a line of code
Every AI project begins with a clear research verdict.
05
DevOps & MLOps
The runtime layer for the Agentic SDLC. Reliable, secure, observable infrastructure keeps your software running at pace. We build and operate the platforms your delivery depends on.
What we build and run
- •CI/CD pipelines and automated test gates
- •Cloud infrastructure (AWS, Azure, GCP)
- •Model deployment and versioning
- •Monitoring, alerting, and observability
- •Rollback paths and disaster recovery
Platform specialists
Dedicated DevOps and SysOps engineers own your runtime environment end to end.
- •Infrastructure-as-code from day one
- •Security-first cloud architecture
- •Agent runtime monitoring and cost control
- •MLOps pipelines for model training and deployment
Dedicated ownership of your runtime, every day.
06
Data Engineering
The knowledge infrastructure agents run on. Agents are only as good as the data they can reach. We build the pipelines, stores, and retrieval systems that make your knowledge usable.
What we build
- •Data ingestion pipelines and ETL workflows
- •Data warehousing and lake architecture
- •RAG pipelines and retrieval-augmented systems
- •Vector stores and embedding infrastructure
- •Enterprise knowledge bases and document intelligence
Data Engineers
Dedicated specialists in RAG pipeline design, embedding strategy, and document intelligence.
- •Embedding model selection and tuning
- •Chunking strategy and retrieval optimization
- •Multi-modal document processing
- •Data quality and lineage tracking
Knowledge infrastructure built for agents and teams alike.
07
AI Infrastructure Services
Scientific and AI workloads demand compute, storage, and platform architectures built for peak throughput and your workload profile. We design and operate high-performance environments for simulation, training, and large-scale inference.
What we build
- •GPU cluster design and deployment (on-prem and cloud)
- •SLURM, Kubernetes, and batch scheduling environments
- •Storage engineering for AI-scale datasets, checkpoints, and model artifacts
- •Platform engineering for training, inference, and MLOps runtimes
- •ML training pipelines at scale
- •Bioinformatics and simulation workload optimization
- •Cost-aware autoscaling for bursty research compute
AI infrastructure engineers
Specialists who profile workloads, size clusters correctly, and integrate compute, storage, and platform layers with your existing lab and enterprise systems.
- •Workload profiling and cluster right-sizing
- •Multi-GPU and distributed training architecture
- •Storage engineering for high-throughput AI data paths
- •Platform engineering for scalable training and inference environments
- •Hybrid cloud burst for peak demand with efficient capacity utilization
Compute sized for your science and your budget.
Technology
The stack we work in.
We work across the full modern stack and choose tools based on what's right for your system, your team, and your long-term goals.
Cloud
AWS · Azure · GCP · Multi-cloud architecture · Serverless
AI / ML
Claude · OpenAI · TensorFlow · PyTorch · LangChain · LlamaIndex
Frameworks
React · .NET · Python · Node.js · FastAPI · Next.js
Databases
PostgreSQL · MongoDB · Redis · Pinecone · Weaviate · Snowflake
Integration
REST APIs · GraphQL · Message Queues · Event-driven architecture
DevOps
GitHub Actions · Docker · Kubernetes · Terraform · Datadog
Pricing
Module-based fixed bid. You buy outcomes with clear deliverables.
Every engagement is scoped into discrete modules with fixed prices and defined deliverables. You know what you're getting, when you're getting it, and exactly what it costs before the first line of code is written.
The Agentic SDLC compresses delivery timelines and captures that efficiency as a quality and speed advantage for you. You buy outcomes: working software with defined deliverables and predictable cost.
Predictable budgets
Fixed-bid modules give you clear pricing upfront. Scope changes go through a formal change order that is transparent, agreed-upon, and documented.
AI efficiency captured as margin
When agents accelerate delivery, that efficiency goes to you. Speed becomes a quality and timeline advantage built into every module.
Faster delivery at the same price
The Agentic SDLC compresses timelines. You get production-ready software faster at the same predictable module price.
Ready to scope your engagement?
Tell us what you're building. We'll tell you which service lines apply, how the Pod would be staffed, and what a module-based roadmap would look like.