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Duspat

Data & AI Consultancy

Data and AI systems that reach production — and stay there.

Duspat is a specialist Data and AI consultancy. We advise, architect, build and deploy governed cloud data platforms, agentic AI workflows and production-grade data services — on AWS, Azure, Databricks, Snowflake and Claude-enabled delivery.

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  • Nearly 20 years

    Across software engineering, cloud architecture and data platforms

  • AWS, Data & AI

    Solutions architecture, data platforms and AI systems design

  • Enterprise delivery

    Life sciences, financial services, aviation, media and digital platforms

  • Governed by design

    Access, lineage, auditability and production readiness from day one

What we do

Strategy, platforms, pipelines and AI — taken all the way to production.

six services that compose into a single delivery spine. Most engagements begin at one point on it and move along it, which is the point: advice you cannot build is just an opinion.

Platform expertise

The platforms we build on.

We are independent: no reseller agreements, no partner quotas, and no margin on the platform you choose. Recommendations are made on the merits.

Cloud

Architecture, networking and infrastructure across the major providers.

  • AWS
  • Azure
  • GCP
Data platforms

Lakehouse and warehouse platforms, modelled for the workloads you actually run.

  • Databricks
  • Snowflake
  • Redshift
  • BigQuery
  • PostgreSQL
  • Apache Iceberg
AI

Retrieval, agentic workflows and assistants, designed to be governed.

  • Claude
  • Amazon Bedrock
  • RAG & embeddings
  • pgvector
Pipelines & streaming

Batch, streaming and event-driven movement of data, with quality checks that fire.

  • Apache Airflow
  • DBT
  • PySpark
  • Apache Kafka
  • Kinesis
  • AWS Glue
Delivery

Infrastructure as code and pipelines that make the next release boring.

  • Terraform
  • CloudFormation
  • Docker
  • Kubernetes
  • GitHub Actions
  • GitLab CI

Why Duspat

Specialist, senior, and accountable for what ships.

Five claims, each one specific enough that you could hold us to it.

Senior, hands-on architecture
You speak to the person who designs the system and then writes the code. There is no pyramid, no bench, and no handover to a delivery team you have not met.
Production-grade engineering
Infrastructure as code, automated deployment, observability and a rollback path — designed in from the first commit, because retrofitting them is where budgets go to die.
Data governance built in
Classification, access control, lineage, retention and audit decided during design, while they are still cheap. Retrofitting governance is the most expensive way to buy it.
Cloud-native delivery
Event-driven, serverless and container-based architectures on AWS and Azure, sized for the workload you actually have rather than the one on the vendor slide.
Practical AI adoption
Not a strategy deck. Use-case triage, a control model your risk team will sign, enablement for the people doing the work, and measurement against the case that funded it.

Agentic AI

Agentic AI is only useful when it is bounded.

We design agentic workflows with explicit tool boundaries, human review points, audit trails and cost controls — then integrate them into the systems where the work actually happens.

We also use Claude-enabled workflows in our own engineering practice, which is why we can be specific about where they help — and honest about where a deterministic pipeline is simply the better answer.

Agent workflows and orchestration
Multi-step workflows with explicit tool boundaries — what an agent may call, what it may never call, and where a human has to sign.
RAG and knowledge systems
Retrieval over your own material, with entitlement-aware search so the model cannot surface what the reader is not cleared to see.
AI assistants in the real workflow
Assistants integrated into the systems where the work already happens, rather than a chat window staff have to remember to visit.
Evaluation and guardrails
An evaluation harness, regression tests, audit logging, rate limiting and cost controls — the things that turn a demo into something you can approve.

Delivery approach

How an engagement runs.

seven stages. Most consultancies stop at deploy — which is exactly why so much of what they build does not survive its first year.

  1. 01

    Discover

    Constraints, data and the decisions already made.

  2. 02

    Assess

    An honest read of maturity, readiness and risk.

  3. 03

    Architect

    A target state with the trade-offs named and costed.

  4. 04

    Build

    Production standards from the first commit, not the last sprint.

  5. 05

    Deploy

    Repeatable releases with a rollback path that has been tested.

  6. 06

    Govern

    Access, lineage, audit and quality — enforced, not documented.

  7. 07

    Enable

    Standards, runbooks and mentoring — so your team owns it.

Read our delivery approach in full

Selected engagements

Evidence, not testimonials.

Anonymised, sector-level, and written for someone who will ask how it was actually built. Client names are withheld by design — the constraint and the architecture are the parts that matter.

  • Life sciences

    A governed RAG system for sensitive research data

    The constraint. Retrieval over sensitive research material, where the model must never surface content the user is not entitled to see.

    The control model was designed before the model was chosen: entitlement-aware retrieval, IAM boundaries and audit logging built in from the first commit. The result was a proof of concept that could credibly become production, rather than a demonstration that quietly stalled at the security review.

    • Amazon Bedrock
    • pgvector
    • Lambda
    • API Gateway
    • IAM
    • CloudWatch

  • Enterprise

    Large-scale migration off a legacy warehouse

    The constraint. Very large production tables with live dependencies, and no acceptable downtime window.

    Migration designed and executed into Redshift, Snowflake and PostgreSQL using change-data-capture and a phased cutover, so the business kept reading from a consistent view throughout. The estate ended on a platform the in-house team could actually operate.

    • AWS
    • Redshift
    • Snowflake
    • PostgreSQL
    • CDC

  • Media

    Event-driven platform for financial royalty reporting

    The constraint. Highly variable volumes, financial accuracy, and reporting that has to withstand an audit.

    An event-driven AWS architecture that scales with volume rather than with headcount, and where every figure can be traced back to the event that produced it.

    • API Gateway
    • Lambda
    • Step Functions
    • EventBridge
    • DynamoDB
    • Glue

Let’s talk about your Data and AI priorities.

Bring a problem, an architecture, or an initiative that has stalled. The first call is technical, and you will speak to the person who would do the work.

Book a consultation

30 minutes, no pitch deck.