AI & Machine Learning
AI you can put in production.
Most enterprise AI stalls between the demo and production. We ship the parts that matter: evaluation harnesses, retrieval quality, guardrails, and MLOps — engineered for regulated environments where hallucinations, drift and data exfiltration are not acceptable failure modes.
/ Capabilities
Retrieval-augmented generation over your knowledge, secure by default, with prompt-injection defence and per-tenant grounding.
Feature stores, training pipelines, and model registries on SageMaker, Vertex AI or Databricks — with reproducible experiments.
Contract analysis, KYC/AML document extraction, claims processing, and summarisation with human-in-the-loop review.
Quality inspection, video analytics, OCR, and edge-deployed models for manufacturing, retail and physical-security use cases.
Bounded agents that call your APIs and take action inside guardrails — with audit trails, approvals and rollback.
Bias testing, red-team evaluation, safety benchmarks and continuous monitoring aligned to EU AI Act and NIST AI RMF.
There is no single 'best' LLM. We choose per workload — favouring open models where data-sovereignty matters, frontier models where reasoning is scarce — and always behind a common gateway with observability, cost control and safety filters.
We decline projects where AI is a marketing wrapper for a poorly-scoped process. We would rather ship a smaller, defensible workflow than a headline demo that breaks under load.
/ Frameworks & Standards
/ Case Studies · Measurable Outcomes
Representative engagements from Solvin's ai & machine learning practice. Client identities are withheld under NDA; industry, scope and results are as-delivered.
Generative-AI copilot for relationship managers, grounded on internal product KB with strict PII redaction and audit trail.
Outcomes
Claims-adjudication ML pipeline with document intelligence and human-in-the-loop review for exceptions.
Outcomes
Predictive-maintenance ML on vibration + thermal telemetry across 400 CNC machines with edge inference.
Outcomes
Never. Every engagement uses tenant-isolated infrastructure and — when using hosted providers — routes only through zero-retention endpoints or self-hosted models.
Reproducible pipelines, versioned prompts and models, automated evaluation on every change, observability with drift alerts, and an on-call runbook. If it can't be shipped like software, it isn't ready.
/ Practice areas
Each practice area has its own scope, methodology and deliverables. Explore in depth below.
CRISP-DM aligned ML delivery — from business problem framing to production models on tabular, text and vision data.
Learn moreProduction-grade MLOps aligned to Google's MLOps maturity model — pipelines, registries, monitoring and governance.
Learn moreRAG systems, fine-tuning, agentic workflows and LLM evaluation — built with production safety in mind.
Learn moreLakehouse architectures, streaming pipelines and data-contracts that make ML and analytics dependable.
Learn moreAI governance programmes aligned to NIST AI RMF 1.0 and ISO/IEC 42001 — risk, fairness, explainability and oversight.
Learn moreA senior practice lead — not a sales rep — will respond within one business day.
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