Evaluating ML vendors without knowing which architecture fits your data?
Internal team wants to build ML but does not know where to start?
Machine Learning Consulting Services
Before you invest in building a machine learning system, you need to know whether your data supports the use case, which approach fits the problem, and what the production architecture should look like.
We help product teams, engineering leaders, and business owners answer those questions -- with a structured assessment, an architecture recommendation, and a build plan you can execute with your own team or with us.
ML feasibility assessment on your actual data
Architecture design for ML systems integrated with your existing stack
Use case prioritisation -- which problems are worth building for
Vendor and tool evaluation for your specific requirements
RaftLabs provides machine learning consulting for product teams and engineering leaders who need strategic guidance before committing to an ML build. Our consulting engagements include data feasibility assessment, ML use case prioritisation, production architecture design, vendor evaluation, and team capability review. For teams with in-house engineers, we provide the ML architecture and strategy. For teams without in-house ML capability, we can move directly into development.
Most ML projects fail before they start
The failure point is not the model. It is the assumptions made before any code was written: that the data was clean enough, that the use case was well-defined, that the model output would reach the right people, that the engineering team could maintain the system after delivery.
Machine learning consulting surfaces these problems before they become expensive. A structured assessment takes weeks. Reversing a failed ML architecture takes months and burns engineering credibility.
What we cover
ML use case assessment
Evaluating whether your proposed ML use case is technically feasible given your current data, defining the right problem formulation, identifying which ML approach fits, and sizing the expected accuracy and business impact. Most assessments reveal either that the problem is simpler than expected (a rule-based system would work) or more constrained than assumed (the data doesn't support the prediction).
Data audit and readiness
Review of your available data for volume, quality, labelling completeness, feature coverage, and temporal range. Identification of data gaps that need to be filled before model training. Assessment of data collection changes needed to make future models more accurate. Most businesses have more usable data than they think -- but it needs to be in the right shape.
ML architecture design
Production architecture for your ML system: data pipeline design, model serving infrastructure, integration with your existing applications, monitoring and retraining strategy. The architecture defines how predictions get from the model to the people who act on them -- which is where most ML systems fall short.
Vendor and tool evaluation
Independent evaluation of ML platforms, data infrastructure tools, and cloud AI services for your specific use case and budget. We have no vendor relationships -- our recommendation is based on what fits your problem, not what pays us a referral. Covers: build vs. buy decisions, cloud ML services (AWS SageMaker, GCP Vertex, Azure ML) vs. custom, and open-source vs. commercial tooling.
ML team capability review
Assessment of your in-house team's ML capability against the requirements of your proposed project. Identification of skills gaps (data engineering, model training, MLOps, deployment). Recommendations for team structure -- whether to hire, train, or partner for specific roles. Most teams are closer to ML-ready than they think; the gap is usually in data engineering and MLOps, not modelling.
ML roadmap and prioritisation
For organisations with multiple ML use cases, a structured prioritisation framework: ROI potential, data readiness, implementation complexity, and strategic fit. A sequenced roadmap that builds on each use case -- so the data infrastructure built for use case one accelerates use case two. The output is a phased ML programme you can execute with predictable investment.
Know before you build.
Tell us the use case you are considering, the data you have, and what the decision needs to improve. We will tell you whether it is worth building.
Related services
Machine Learning Development -- full-stack ML system build after strategy is confirmed
Predictive Analytics Services -- forecasting and risk scoring systems
AI Development Services -- custom AI products across the full AI stack
Generative AI Consulting -- strategy for LLM and generative AI adoption
AI PoC Development -- proof of concept before committing to full ML development
Frequently asked questions
Machine learning consulting is the strategic and architectural work that happens before building an ML system. It covers -- which ML use cases are feasible given your data, which approach fits the problem, what the production architecture should look like, which tools and platforms to use, and how to structure the team and roadmap. Consulting is valuable when you need to make architecture decisions without having ML expertise in-house, or when you want an independent assessment of a proposed ML approach before committing budget.
Consulting makes sense when the use case is not well-defined, the data situation is uncertain, or internal stakeholders disagree on the approach. A short consulting engagement (2--4 weeks) produces clarity on what to build and why, which prevents expensive course-corrections during development. For teams with a clear use case and confirmed data, moving directly to development with an embedded ML engineer is often faster and cheaper than a separate consulting engagement.
A data audit (volume, quality, labelling, and coverage), a use case evaluation (is the problem solvable with ML given the available data?), a baseline model test (can we demonstrate the approach works before committing to full development?), an architecture recommendation (what production system should this become?), and a build roadmap (phases, timeline, and team requirements). The output is a structured recommendation document -- not a PowerPoint deck, a working document you can act on.
Yes. Many consulting engagements involve working alongside your in-house engineers -- providing ML architecture guidance, reviewing model approaches, and advising on infrastructure decisions while your team does the implementation work. We can also provide hands-on training for engineering teams new to ML who want to build capability rather than rely on external development.
A focused feasibility assessment for a single use case takes 2--3 weeks. A broader ML strategy engagement covering multiple use cases, data architecture, and team roadmap takes 4--8 weeks. Most consulting engagements end with a clear build recommendation and the option to move directly into development with us.
A focused feasibility assessment for a single use case typically runs $8,000--$20,000. A broader ML strategy engagement covering multiple use cases and architecture design runs $20,000--$50,000. Consulting engagements are fixed-price with a defined scope and output -- not open-ended retainers.