Platform, product, data, AI, and geospatial — staffed to the problem.

The problem

Releases are slow, fragile, or blocked by manual steps. Security and quality checks land too late — or never. On-call lives in the pipeline.

Best fit

Fintech, banks, and product teams that need reliable delivery without a large platform org.

How we work

• Map your current build → test → deploy flow and failure modes
• Introduce shift-left scanning (SAST, SCA/Xray, SonarQube, Veracode) where it fits
• Wire secrets and policy via Vault or cloud-native secret stores
• Automate zero-downtime deploy and rollback so production changes are reversible
• Document runbooks so your team can operate without us

What you get

• Production-ready pipelines in your CI system of record
• Security and quality gates with clear pass/fail criteria
• Deploy/rollback workflows and environment promotion paths
• Handoff docs and optional team training

The problem

Workloads live on brittle VMs, snowflake environments, or half-migrated containers. Every environment is different; every deploy is a negotiation.

Best fit

Teams moving to AWS, consolidating after acquisition, or replacing ad-hoc infra with a platform you can own.

How we work

• Baseline architecture, cost, and risk before moving or rebuilding
• Infrastructure as code (Terraform / AWS CDK) for repeatable environments
• Container and orchestration paths — Docker, Podman, Kubernetes, Helm
• Hardened AMIs and OS baselines (RHEL/Ubuntu) where compliance matters
• Phased cutover plans finance and security can review

What you get

• IaC repositories and environment topology
• Containerized services and deployment manifests
• Migration or modernization plan with rollback points
• Operational runbooks for your team

The problem

Cloud invoices grow faster than product value. Nobody owns unit economics. Optimization is a one-off spreadsheet, not a system.

Best fit

Scale-ups and enterprises with material AWS spend who need engineering-led savings, not only dashboards.

How we work

• Establish spend baselines by service, team, and environment
• Identify waste: idle capacity, wrong instance families, storage and data transfer leaks
• Validate high-impact architecture moves (e.g. Graviton for EMR-class jobs)
• Automate guardrails and reporting so savings stick after the engagement

What you get

• Cost baseline and prioritized savings roadmap
• Implemented quick wins and measured before/after
• Optional automation for ongoing FinOps hygiene
• Executive-friendly summary for finance stakeholders

The problem

Security reviews block releases. Scanners exist but are not in the critical path. Payment or bank data needs evidence of control — not slideware.

Best fit

Payment processors, banks, and fintechs under audit pressure who still need to ship.

How we work

• Embed CVE and code quality scanning early in CI
• Centralize secrets and access with Vault or equivalent
• Align infrastructure and pipelines with PCI-style control expectations
• Support pipeline and platform incidents when reliability is non-negotiable

What you get

• Security-integrated CI/CD configuration
• Secrets and access model documentation
• Compliance-oriented runbooks and evidence paths
• Optional ongoing platform support

The problem

You need reliable backend capacity — new product surfaces, partner APIs, or domain services — without stalling the roadmap for months of hiring.

Best fit

Product and enterprise teams standardizing on Java/Spring who need senior delivery, not a staff-augmentation free-for-all.

How we work

• Clarify domain boundaries, API contracts, and non-functionals (latency, auth, audit)
• Implement Spring Boot services with clear modules, tests, and observability hooks
• Integrate with your identity, messaging, and data stores as needed
• Ship behind CI/CD with environments your team can promote

What you get

• Working Spring Boot services and API documentation
• Automated tests and pipeline-friendly builds
• Deployment configuration aligned with your platform
• Handoff notes so internal teams can extend the work

The problem

Production Spring apps are aging, patched ad hoc, or stuck on EOL runtimes. Rewrites scare the business; neglect scares security.

Best fit

Teams with revenue-critical Spring systems who need steady engineering without a full permanent platform hire.

How we work

• Inventory runtime, dependencies, and operational risk
• Plan Spring Boot / JDK upgrades with regression and canary strategy
• Address performance hotspots and incident-prone paths
• When migrating legacy Java, use phased cutovers instead of big-bang rewrites

What you get

• Upgrade or migration plan with milestones
• Patched, supported runtime and dependency baseline
• Performance and reliability improvements as scoped
• Runbooks and knowledge transfer for your team

The problem

Data lands late, inconsistently, or without tests. Every team builds one-off scripts. Nobody trusts the dashboard — or the feature that depends on it.

Best fit

Product and analytics orgs that need a reliable data plane under engineering ownership.

How we work

• Map sources, SLAs, and consumers before writing jobs
• Design pipelines with idempotency, retries, and observable failure modes
• Add quality checks and clear ownership of data products
• Orchestrate with tooling that fits your stack (cloud-native or open source)

What you get

• Production pipelines with monitoring and alerting
• Transform logic and data contracts as documented
• Quality checks and runbooks for incidents
• Handoff for your data or platform team

The problem

The warehouse or lakehouse is expensive, under-governed, or underused. Analysts wait; engineers firefight jobs; finance asks why the bill grew again.

Best fit

Teams investing in Snowflake, Databricks, or AWS data platforms who need engineering outcomes, not only architecture slides.

How we work

• Baseline architecture, roles, and spend
• Model data products and access patterns for real consumers
• Implement jobs, environments, and cost controls
• Introduce lightweight governance (access, lineage, quality) without process theater

What you get

• Platform topology and environment setup
• Core pipelines/jobs and documentation
• Cost and access recommendations implemented as scoped
• Enablement for analysts and engineers

The problem

Demos impress stakeholders; production fails on latency, cost, safety, or integration. The model is not the hard part — the system is.

Best fit

Product teams shipping AI into an existing application who need senior full-stack and platform judgment.

How we work

• Scope the user workflow and success metrics before model choice
• Build APIs, auth, and persistence around the model, not the reverse
• Add basic evals, logging, and cost visibility from day one
• Deploy with the same CI/CD and observability standards as any backend

What you get

• Production-ready AI feature or service
• Integration docs for your product team
• Monitoring and cost baselines
• Optional retainer for iteration after launch

The problem

RAG prototypes hallucinate on real corpora, ignore access control, or cost too much to run. Agents work in notebooks and fail in production.

Best fit

Teams with internal knowledge, support, or ops workflows that need grounded AI — not generic chatbot wrappers.

How we work

• Design chunking, embedding, and retrieval for your corpus and latency budget
• Enforce authz on retrieved context and tool calls
• Add evaluation sets and regression checks for quality
• Operate vector stores and pipelines with clear ownership

What you get

• Retrieval / agent system integrated with your apps
• Index and re-index pipelines
• Eval harness baseline and ops notes
• Handoff for ongoing improvement

The problem

Every team reimplements LLM gateways. Token spend is invisible until finance escalates. There is no shared path to secure, multi-app AI infrastructure.

Best fit

Organizations running multiple AI features who need shared infrastructure and cost discipline.

How we work

• Define shared gateway, auth, and multi-provider strategy
• Instrument usage, quality signals, and spend by app/team
• Add guardrails for rate limits, budgets, and failover
• Ship APIs/SDKs your product teams can adopt

What you get

• Platform components and integration path
• Usage and cost visibility surfaces
• Operational runbooks
• Optional ongoing platform support

The problem

Engineering teams struggle to move beyond simple API prompts to production RAG, agentic workflows, and token FinOps without structured upskilling.

Best fit

Engineering orgs, tech leads, and enterprises looking to rapidly upskill their teams to build production-grade AI applications.

How we work

• Baseline team technical capabilities and target enterprise use cases
• Deliver interactive coding bootcamps covering RAG, vector DBs, and LLM evaluation
• Train on token cost optimization, local model deployment, and security guardrails
• Provide architecture blueprints and reusable starter kits for immediate execution

What you get

• Customized 1-3 day technical workshop curriculum and hands-on labs
• Enterprise AI architecture reference guide and starter codebases
• Evaluation benchmarks and safety compliance checklists
• Post-workshop Q&A and architecture review sessions

The problem

Location and imagery data sit in silos: desktop GIS projects, ad-hoc scripts, and PDFs. Teams cannot share live layers, automate processing, or embed maps into products with confidence.

Best fit

Government, utilities, agriculture, infrastructure, environment, and product teams who need production GIS and EO — not one-off analysis decks.

How we work

• Scope use cases, data sources (satellite, aerial, drone, IoT, survey), and accuracy/SLA needs
• Design spatial data models, coordinate systems, and storage (vector, raster, tile caches)
• Build remote-sensing / EO pipelines — ingest, preprocess, classify or change detection as scoped
• Deliver web GIS and map APIs your products and field teams can use day to day
• Operationalize on cloud or hybrid infra with the same CI/CD and access control as other systems

What you get

• Spatial data platform design and implementation as scoped
• Imagery / remote-sensing processing workflows and documentation
• Web maps, tiles, or geospatial APIs integrated with your stack
• Handoff runbooks for ongoing data updates and operations

The problem

Off-the-shelf tools do not match how your staff actually work. You need mobile apps for the field, an admin web console for HQ, and reliable auth and reporting — without a multi-year internal build.

Best fit

Schools, institutions, and organizations that need purpose-built mobile + web systems — not a generic template CRM.

How we work

• Map roles, workflows, and offline/online constraints (teachers, staff, admins, field users)
• Design and build mobile clients (Android + iOS) with secure auth and clear UX
• Deliver companion web admin portals for configuration, reporting, and operations
• Implement location-aware features, attendance, and HR workflows where required
• Ship with backend APIs, environments, and handoff so you can operate after go-live

What you get

• Production Android and iOS apps (store or enterprise distribution as scoped)
• Admin / staff web application with role-based access
• APIs, data model, and deployment path
• Documentation and optional training for operators

The problem

Your site is outdated, slow on mobile, or hard to update. First impressions lag the quality of your actual product or institution.

Best fit

Companies, schools, and products that need a credible web presence without a full internal marketing eng team.

How we work

• Clarify audience, message, and conversion goals (leads, info, enrollment, trust)
• Design a clean, professional visual system that matches your brand
• Build responsive pages with performance and accessibility in mind
• Deploy with a simple update path and analytics basics as scoped

What you get

• Designed and developed website (responsive)
• Deployed production site with handoff notes
• Optional CMS or content update guidance
• Contact / inquiry flows as needed

The problem

Founders face long hiring cycles or unreliable agencies when building core product infrastructure, leading to missed market windows or technical debt.

Best fit

Startups, spin-offs, and founders seeking a proven engineering partner to take products from concept to revenue.

How we work

• Define product scope, core domain entities, and non-negotiable MVP milestones
• Architect cloud-native backends, admin portals, and cross-platform clients
• Establish CI/CD pipelines, automated testing, and SOC/DPDP-compliant data security
• Provide fractional engineering management until full in-house team transition

What you get

• Production MVP ready for customer onboarding and investor demos
• Fully documented cloud infrastructure, CI/CD, and database schemas
• Role-based admin dashboards and customer management portals
• Smooth handoff documentation and technical hiring assistance