MACHINE LEARNING CONSULTING
PROPELOO engineers machine learning consulting solutions — from architecture and design through development, testing and deployment. We apply the same engineering rigour to every engagement: clear requirements, milestone-based delivery, and working software at every sprint. Our machine learning consulting implementations are built to production standards from day one.
Machine learning consulting projects fail for predictable reasons: requirements that were unclear at the start, architectural decisions that optimised for speed over correctness, and technical debt that compounds until the system cannot be changed without breaking something. PROPELOO starts every machine learning consulting engagement with the architecture — before a line of code is written — because the cost of getting it right early is always less than the cost of getting it wrong. We deliver working software at every milestone, with IP that transfers to you at each invoice.
Production machine learning consulting requires correct decisions across multiple engineering domains simultaneously.
System architecture documented in Architecture Decision Records — every technology choice justified against requirements. Data model, API contracts and integration patterns designed before implementation begins.
Feature development in two-week sprints with working software demonstrated at each sprint end. Every PR reviewed, every feature tested, every deployment to staging before production.
Third-party API integrations, data pipeline connections, webhook handling, retry logic and graceful degradation. Every integration has error handling and is tested against real API responses.
Authentication and authorisation, input validation, dependency scanning in CI, secrets management, OWASP best practices and threat modelling for security-sensitive components.
Unit, integration and end-to-end tests in CI. Coverage enforcement for critical business logic. Performance testing before launch. Security scanning on every build.
CI/CD pipeline, infrastructure as code (Terraform), containerised deployment, environment management and monitoring from day one.
Every machine learning consulting project has the same fundamental challenge: translating what the business needs into software that reliably delivers it. The engineering is the easy part. The hard part is making sure the right thing is being built.
A sprint that starts without agreed architecture and scope will spend the first three days making decisions that should have been made in discovery. Paid discovery (5-10 days) that produces architecture documentation, data model and milestone plan is not optional overhead — it is the difference between a sprint team that executes and one that debates.
Axiom:
A sprint that produces completed tickets but no deployable software has not made progress. Every sprint ends with software that can be deployed to staging and demonstrated. Velocity is measured in shipped features, not story points.
Axiom:
Requirements change. The question is whether they are managed transparently. Every scope change is documented as a change order: what is changing, estimated effort impact, timeline adjustment. This protects both parties.
Axiom:
Every deliverable — code, architecture documentation, design files — transfers to the client at the milestone invoice. No ongoing PROPELOO dependency. No licence fees. The codebase is yours from day one.
Axiom:
These are the questions we work through in the architecture phase.
Impact: Build for competitive differentiators. Buy or integrate for commodity functionality. The correct split depends on what makes your system unique.
Impact: Start with a modular monolith. Extract services when demonstrated need justifies operational overhead.
Impact: PostgreSQL for the primary data store unless access patterns specifically require another approach.
Impact: Async processing for operations that do not need to be in the response path. Synchronous for reads and real-time requirements.
Impact: Managed auth for speed to market unless compliance requirements mandate self-hosted.
Impact: ECS Fargate or Cloud Run for most applications. Kubernetes when the ecosystem provides genuine value.
Full production machine learning consulting from architecture through deployment — milestone-based delivery, automated testing, CI/CD and documentation.
Rapid MVP of the core machine learning consulting functionality — focused scope, 8-12 week delivery, production-quality from the start.
Migrate or rebuild an existing machine learning consulting system — strangler fig migration, zero-downtime deployment, data migration.
Machine learning consulting API development and third-party integration — OpenAPI spec, authentication, rate limiting and developer documentation.
Architecture review of existing machine learning consulting system — technical debt assessment, security review, performance bottlenecks and remediation roadmap.
Performance engineering for machine learning consulting at scale — load testing, bottleneck identification, caching strategy and infrastructure scaling.
Every technology choice is justified against the specific requirements of the system.
Stack: Node.js (TypeScript), Go, Python, PostgreSQL, Redis
Stack: React, Next.js, TypeScript, Tailwind CSS, TanStack Query
Stack: AWS / GCP, Terraform, Docker, Kubernetes / ECS, GitHub Actions
Stack: Jest / Vitest, Playwright, k6 (load testing), Sentry, OpenTelemetry
Stack: REST + OpenAPI, GraphQL, WebSocket, gRPC
Stack: Auth0, Clerk, JWT, OAuth 2.0
Every PROPELOO engagement includes security as a standard part of delivery.
Secure session management, RBAC at the API layer, JWT with correct expiry and rotation, OAuth 2.0 integration and audit logging for sensitive operations.
Schema validation at the API boundary, parameterised queries (no SQL injection), output encoding for XSS prevention and file upload validation.
No secrets in source code, AWS Secrets Manager or equivalent, rotation without downtime and least-privilege IAM per service.
Automated vulnerability scanning (Dependabot/Snyk) in CI, pinned dependency versions, no known critical CVEs in production builds.
Encryption at rest for sensitive fields, TLS 1.3 in transit, PII fields identified with appropriate access controls and retention limits.
STRIDE threat modelling at the architecture phase — trust boundaries, attack surface identification and risk-prioritised mitigation before implementation.
Architecture, data model, technology selection, milestone plan. Deliverable: architecture document.
Project scaffold, CI/CD, environments, auth, core data model.
Feature development in 2-week sprints. Working software every sprint.
Third-party integrations, API connections, data pipeline.
Test suite, performance testing, security review.
Zero-downtime production deployment, monitoring, runbooks.
Documentation, knowledge transfer, optional retainer.
Timeline depends on scope. A focused MVP (core features, single integration): 8-12 weeks. A full-featured production system: 4-8 months. We always start with a discovery sprint (5-10 days) that produces a milestone plan with realistic timelines before any development begins.
Requirements change — this is expected. Every scope change is processed as a change order: documented, estimated, agreed before work begins. This keeps budget and timeline visible throughout. We welcome changes when they are managed transparently.
You do, unconditionally and from day one. Every deliverable transfers at the milestone invoice — code, documentation, design files. No ongoing PROPELOO licence or dependency.
Yes. Before committing to scope, we review the existing codebase to understand its current state, technical debt and architecture. We are honest about what we find — existing technical debt affects delivery timelines.
We select technology based on requirements, not habit. Primary stack: TypeScript (Node.js/React) for most web applications, Go for high-performance services, Python for ML/data. We do not introduce new dependencies without justification.