Services
Specialized engineering for hard technical problems
Five areas of deep expertise. Each engagement is scoped to your problem — not sold by the hour.
Scientific Computing
Design and optimize the computational infrastructure behind research — HPC clusters, workflow engines, and large-scale data processing.
Research teams often outgrow the systems they started on. Jobs queue for days, pipelines fail silently at scale, and compute costs climb without a clear owner. The bottleneck stops being science and starts being infrastructure.
- University research computing groups
- Core facilities and shared HPC environments
- Labs processing genomic, imaging, or simulation data
- Teams migrating between on-prem clusters and cloud
- HPC and cluster architecture review with prioritized recommendations
- Workflow orchestration setup (Slurm, Nextflow, Snakemake, WDL)
- Parallelized, resumable data-processing pipelines
- Job scheduling, resource, and cost tuning
- Runbooks and documentation your team can maintain
- Multi-fold reduction in end-to-end pipeline runtime
- Lower compute spend through right-sizing and scheduling
- Reproducible, restartable jobs that survive failures
- Researchers unblocked from infrastructure firefighting
- 1Profile current workloads and identify bottlenecks
- 2Design target architecture and migration path
- 3Implement, benchmark, and validate against real data
- 4Hand off with documentation and knowledge transfer
Bioinformatics
Reproducible multi-omics pipelines and scientific software — from microbiome and metagenomics to production-grade analysis tooling.
Analysis code that works once on a laptop rarely survives contact with new datasets, collaborators, or publication review. Results are hard to reproduce, pipelines are brittle, and every new project restarts from scratch.
- Academic labs and translational research groups
- Biotech R&D and computational biology teams
- Microbiome, genomics, and multi-omics programs
- Groups preparing pipelines for publication or regulatory review
- Reproducible Nextflow / Galaxy pipelines with containerized tools
- Microbiome, metagenomics, and multi-omics workflow development
- Version-pinned, testable scientific software packages
- Data provenance, QC, and validation reporting
- Pipeline documentation suitable for methods sections
- Analyses that reproduce exactly across machines and time
- Faster turnaround on new datasets and collaborations
- Publication- and audit-ready methods and provenance
- Reduced dependence on a single person's local setup
- 1Review current analysis and reproducibility gaps
- 2Refactor into containerized, parameterized pipelines
- 3Validate outputs against known results
- 4Document and transfer ownership to your team
Cloud Infrastructure
Scalable, automated cloud systems on AWS and Google Cloud — with cost control, security, and infrastructure-as-code baked in.
Cloud bills grow faster than usage, environments drift from documentation, and deployments become risky manual events. Without automation, scaling means more fragility, not more capacity.
- Startups scaling beyond their initial architecture
- Research groups moving workloads to the cloud
- Teams without a dedicated infrastructure engineer
- Organizations facing rising or opaque cloud costs
- Cloud architecture design and migration plans
- Infrastructure-as-code (Terraform) and CI/CD pipelines
- Kubernetes and container orchestration setup
- Cost analysis with concrete optimization actions
- Monitoring, logging, and alerting foundations
- Meaningful reduction in monthly cloud spend
- Repeatable, reviewable, one-command deployments
- Elastic capacity that scales with real demand
- Infrastructure that matches its documentation
- 1Audit current architecture, cost, and risk
- 2Design target state and phased migration
- 3Automate with infrastructure-as-code and CI/CD
- 4Validate, monitor, and document the system
Artificial Intelligence
Production AI systems — LLM applications, retrieval, document processing, and structured extraction that hold up outside the demo.
AI prototypes are easy; reliable AI systems are not. Hallucinations, unstructured outputs, and unpredictable costs keep promising demos from reaching production. The gap is engineering, not model access.
- AI startups building on top of foundation models
- Teams automating document and data workflows
- Organizations with unstructured data to extract or search
- Companies moving an LLM prototype toward production
- LLM applications with evaluation and guardrails
- Retrieval-augmented generation (RAG) and semantic search
- Document processing and structured data extraction pipelines
- Workflow automation with human-in-the-loop checkpoints
- Cost, latency, and quality monitoring
- Reliable, structured outputs you can build on
- Reduced manual document and data-entry effort
- Predictable inference costs and latency
- A clear path from prototype to production
- 1Define the task, success metrics, and evaluation set
- 2Prototype and measure against real inputs
- 3Harden with guardrails, retries, and observability
- 4Deploy and establish ongoing quality monitoring
Software Engineering
Production software built to last — APIs, data platforms, and web applications with the testing and rigor of a senior engineering team.
Internal tools and research software often carry no tests, no documentation, and one person who understands them. As the team grows, that debt turns into a liability that slows every new feature.
- Scientific software companies and research groups
- Startups needing senior engineering without a full hire
- Teams inheriting or hardening legacy systems
- Organizations building data-intensive applications
- Production APIs and services (FastAPI, Django)
- React / Next.js web applications and dashboards
- PostgreSQL data models and query optimization
- Automated testing, CI, and code-quality tooling
- Architecture reviews and technical documentation
- Maintainable software with real test coverage
- Faster, safer iteration for the whole team
- Systems that survive team and staffing changes
- Clear architecture and documentation
- 1Assess requirements, constraints, and existing code
- 2Design the architecture and delivery milestones
- 3Build iteratively with tests and reviews
- 4Deploy, document, and support the handoff
How we work together
A clear path from problem to solution
Every engagement follows the same transparent process — starting with a free consultation and ending with a system your team can own.
Free Discovery Consultation
A complimentary conversation to understand the problem before proposing anything.
Technical Assessment
A grounded recommendation on the right approach — not a sales pitch.
Custom Proposal
A transparent proposal so you know exactly what you're getting and why.
Implementation & Support
Collaborative delivery, with documentation and ongoing support when you need it.
Engagement & pricing
How pricing works
Every project is unique. Consulting engagements are customized based on technical requirements, project scope, timeline, and desired outcomes. Schedule a free consultation to discuss your challenges and determine the right approach.
What pricing is based on
- Project scope and technical complexity
- Expected outcomes and business impact
- Timeline and urgency
- Depth of specialized expertise required
- Level of ongoing support
Engagements are priced by scope and outcome — not by the hour. Every engagement begins with a free initial consultation.
Invoicing is handled per the agreed proposal, typically via ACH or wire transfer, with card payment available for smaller engagements.
Project Engagement
A defined problem with a clear scope, timeline, and deliverables. Best when you know what needs to be built or solved.
Technical Assessment
A focused review of your architecture, pipelines, or costs, delivered as prioritized, actionable recommendations.
Ongoing Advisory
Retained access to senior expertise for architecture decisions, reviews, and hands-on help as your systems evolve.
Have a hard technical problem?
Every engagement begins with a free consultation. Tell me what you're working on and we'll figure out the right approach together — no obligation.