12 capabilities
From strategy to the model lifecycle.
Each capability explains the problem, Bellot's work, applications, requirements and embedded controls.
01
AI Strategy & Readiness
Teams see opportunities but lack a defensible starting point.
- How Bellot works
- We identify decisions worth augmenting, assess data viability and define architecture, governance and an execution roadmap.
- Applications
- Portfolio prioritization, AI product discovery and build versus buy decisions.
- Expected outcome
- A sequenced portfolio of viable initiatives with owners, dependencies and success criteria.
- Requirements
- Business owners, process context and an initial view of available data.
- Security and oversight
- Risk, privacy, security and human oversight are defined before implementation.
02
Machine Learning
Rules and manual analysis cannot represent complex patterns at operational scale.
- How Bellot works
- We develop, evaluate and integrate supervised or unsupervised models appropriate to the decision.
- Applications
- Classification, scoring, recommendation, clustering and behavioral analysis.
- Expected outcome
- A tested model connected to a measurable workflow, with known limits.
- Requirements
- Representative historical data, target definition and evaluation criteria.
- Security and oversight
- Baseline comparison, reproducible evaluation and monitored failure modes.
03
Predictive Analytics
Planning reacts after capacity, demand or risk has already changed.
- How Bellot works
- We build forecasting pipelines and decision interfaces around uncertainty, seasonality and constraints.
- Applications
- Demand, failure, capacity, churn and risk forecasting.
- Expected outcome
- Earlier planning signals and documented confidence ranges.
- Requirements
- Time series, relevant drivers, sufficient history and process ownership.
- Security and oversight
- Uncertainty remains visible and forecasts do not replace accountable decisions.
04
Intelligent Automation
High volume workflows consume specialists and accumulate inconsistent decisions.
- How Bellot works
- We combine rules, models and orchestration to classify, extract, route and recommend actions.
- Applications
- Document intake, triage, claims, compliance and service workflows.
- Expected outcome
- Less repetitive work, traceable routing and faster exception handling.
- Requirements
- A documented workflow, integration points and exception policy.
- Security and oversight
- High consequence actions require explicit approval and audit trails.
05
Enterprise AI Agents
Knowledge and actions are fragmented across systems, documents and teams.
- How Bellot works
- We create task specific agents with tools, permissions, memory boundaries and evaluation suites.
- Applications
- Operations assistants, internal support, research and controlled system actions.
- Expected outcome
- A governed agent that retrieves context and executes approved tasks.
- Requirements
- Authorized APIs, identity, knowledge sources and action boundaries.
- Security and oversight
- Least privilege, traceability, approval gates and prompt injection defenses.
06
Generative AI
Teams cannot efficiently use large volumes of internal knowledge and content.
- How Bellot works
- We design assistants, RAG systems and generation workflows grounded in approved enterprise sources.
- Applications
- Corporate assistants, semantic search, drafting and knowledge access.
- Expected outcome
- Faster access to grounded information with references and evaluation.
- Requirements
- Curated content, access policy, user groups and quality criteria.
- Security and oversight
- Grounding, content controls, privacy and hallucination testing.
07
Natural Language Processing
Critical information is locked in text, messages and documents.
- How Bellot works
- We build pipelines for extraction, classification, summarization and semantic understanding.
- Applications
- Contract analysis, ticket classification, entity extraction and sentiment signals.
- Expected outcome
- Structured information that feeds workflows, analytics and decisions.
- Requirements
- Representative samples, labels when applicable and domain review.
- Security and oversight
- Bias, language variation and ambiguous cases are measured and reviewed.
08
Computer Vision
Visual inspection is slow, inconsistent or impossible to perform continuously.
- How Bellot works
- When viable, we build systems for detection, classification and visual quality analysis.
- Applications
- Quality inspection, asset condition, document vision and operational monitoring.
- Expected outcome
- Consistent visual signals integrated into existing processes.
- Requirements
- Representative images, capture conditions, labels and operational tolerance.
- Security and oversight
- Performance is validated across conditions and uncertainty is escalated.
09
Data Engineering for AI
Fragmented, low quality data prevents reliable models and analytics.
- How Bellot works
- We design ingestion, transformation, quality, lineage and feature pipelines.
- Applications
- Lakehouse foundations, feature pipelines, streaming and governed datasets.
- Expected outcome
- Trusted, reusable data products prepared for model development and operation.
- Requirements
- Source access, ownership, quality rules and platform constraints.
- Security and oversight
- Access control, minimization, lineage and sensitive data handling by design.
10
MLOps & Model Monitoring
Models degrade or become opaque after deployment.
- How Bellot works
- We implement versioning, deployment, evaluation, telemetry, drift detection and rollback.
- Applications
- Model registries, CI for ML, performance dashboards and controlled releases.
- Expected outcome
- A repeatable model lifecycle with visibility and accountability.
- Requirements
- Deployment environment, service objectives and evaluation datasets.
- Security and oversight
- Change control, rollback, drift thresholds and incident procedures.
11
AI Governance & Security
AI adoption expands faster than policy, ownership and technical controls.
- How Bellot works
- We map AI assets, risks, suppliers, controls and decision rights across the lifecycle.
- Applications
- AI inventory, model risk tiers, security testing and usage policies.
- Expected outcome
- Clear governance that supports adoption without hiding material risk.
- Requirements
- Technology, security, legal and business stakeholders.
- Security and oversight
- Privacy, access, provenance, adversarial testing and human accountability.
12
Custom AI Systems
The operation requires intelligence that packaged products cannot provide.
- How Bellot works
- We combine data, models, software and integration into a purpose built system.
- Applications
- Decision engines, specialized copilots and embedded intelligence.
- Expected outcome
- A maintainable product designed around the company’s process and constraints.
- Requirements
- Product ownership, integration access, data and iterative validation.
- Security and oversight
- Architecture, security, observability and responsible AI are part of the system.
