HomeExpertise

Where we work,and what we work with.

Industry context shapes what gets built. The stack is chosen to match the problem, and it's all standard tooling your team can maintain after handover.

Book a call See the stack

Industries

Where the work shows up

Most projects sit where a business process meets a data problem. These are the contexts we see most often, and the capabilities that usually apply.

Retail and distribution

Forecast demand by SKU and location, optimise inventory, and price against transaction history rather than gut feel.

Machine learningData engineering

Subscription and B2B services

Score renewal risk, unify subscription metrics, and give customer success a ranked list each week.

Machine learningData engineering

Sales organisations

Prioritise pipeline by likelihood to close, enrich the CRM from calls and emails, and forecast from deal behaviour.

Machine learningAI integrations

Finance and operations

Reconcile billing to operations, predict cash position, and flag transactions or processes that drift from normal.

Data engineeringMachine learning

Manufacturing and logistics

Anticipate failures and delays from sensor data, plan capacity, and detect anomalies before they become losses.

Machine learningData engineering

Customer support

Draft replies from your help centre, route by intent, and surface similar resolved cases to whoever picks it up.

AI integrationsMachine learning

Legal, compliance and procurement

Search contracts for clauses and obligations, compare incoming documents to your standards, and extract what matters at volume.

AI integrations

Operations and back office

Process incoming documents, validate against your rules, and post to the system of record, with exceptions routed to a person.

AI integrationsData engineering

Teams changing platforms

Move CRM or operations data to a new tool with every record intact, and a reconciliation report before the old one is switched off.

Data engineering

Stack

The stack,
by capability.

We favour the simplest tool that meets the requirement. Nothing proprietary, nothing that makes leaving expensive.

AI models and frameworks

Model providers and open model infrastructure, selected around accuracy, privacy, latency and cost.

  • OpenAI
  • Anthropic
  • Hugging Face
  • LangChain

Machine learning

Data preparation, modelling and experiment tracking for forecasts, scores and classifications.

  • Python
  • Pandas
  • scikit-learn
  • PyTorch
  • MLflow

Data pipelines and transformation

Ingestion, orchestration, streaming and modelling for reliable, tested data flows.

  • dbt
  • Airflow
  • Prefect
  • Airbyte
  • Kafka

Databases and analytics

Operational databases, warehouses and reporting tools, sized to the volume and access pattern.

  • PostgreSQL
  • BigQuery
  • Snowflake
  • ClickHouse
  • DuckDB
  • Metabase

Application backends

APIs, application data and caching for production systems that connect models and workflows.

  • FastAPI
  • Redis
  • Supabase

Infrastructure and cloud

Deployment and infrastructure as code on standard platforms your team can maintain.

  • AWS
  • Google Cloud
  • Docker
  • Kubernetes
  • Terraform
  • GitHub

Business systems

The systems we connect to, ingest from and write back into.

  • HubSpot
  • Salesforce
  • Stripe
  • Slack

Tell us your industry
and what you're trying to fix.

Book a call

Prefer email? info@vectorel.com