LucidData& AI
Enterprise Data & AI Consulting

Data Platform Modernization and AI-readiness are sold as two projects. They’re one.

The semantic layer your agents need is a byproduct of migrating your data properly. Lucid does both in a single engagement, brings the accelerator at no extra cost, and hands you code you own.

Book an assessmentSee the accelerator
Migrate toMicrosoft FabricDatabricksSnowflakeAWSGCP
The stance

We are a consultancy, not a software vendor. What you buy is the outcome: a modernized data platform, a grounded knowledge layer, and agents your business can trust. When our OpenOntos accelerators fit your situation, we bring them at no extra cost to get you there faster.

Why one engagement, not two

Most firms bill modernization and AI-readiness as two year-long projects.

They’re one project. When you profile a legacy schema and map the fields and constraints properly, you’ve already produced most of the ontology your agents need. Most migrations throw that away the day after cutover. We keep it. You get cloud-native code and a grounded knowledge layer from a single engagement.

  1. Legacy estate
    AI ambitions
  2. One
    engagement
  3. Cloud-native code you own
    Grounded semantic layer
Why Lucid

A senior team with an engine the big firms don’t have.

01

Outcomes, not software

You hire us for a result. OpenOntos comes with it, complimentary. You never pay a license to find out whether the tool works.

02

The accelerator does the repetitive 80%

Profiling, ETL dialect conversion (BTEQ, PL/SQL, stored procs to dbt and PySpark), pipeline generation, ontology drafting. Our people spend their time on the 20% that needs judgment.

03

You own what we build

Plain SQL, dbt, PySpark, ADF and Airflow, living in your repo. Open standards for the knowledge layer. Portable, reviewable, yours forever.

04

One motion, two outcomes

The migration produces the ontology. Cloud-native code and a grounded knowledge layer together, not two consecutive projects.

05

Agents and dashboards that agree

We close the knowledge gap that makes agents hallucinate and the definition gap that makes every dashboard disagree, with one semantic layer read the same way across tools.

How the work is shaped

Assess. Modernize. Ground. Run.

  1. 01

    Assess

    A fixed-fee migration and AI-readiness assessment. We profile what you actually have, surface the DQ landmines and undocumented procs before they blow up a cutover, and tell you what your agents will and won't be able to do. You get a report and a plan whether or not you hire us for the build.

  2. 02

    Modernize

    Lift and shift, or redesign into a modern lakehouse architecture. We move the estate to cloud-native code you own, on Microsoft Fabric, Databricks, Snowflake, AWS, or GCP. ETL dialect conversion, profiling, pipelines. Fixed scope, weeks not quarters.

  3. 03

    Ground

    We turn the migration byproduct into a real, standards-based semantic layer and deploy agents grounded in your certified data.

  4. 04

    Run

    Optional managed service once it's live, watching the pipelines and the agents in production. It's a service, not a license. Cancel anytime.

Open standards, no lock-in

Built to standards you can leave with.

Your code lives in your repo. The knowledge layer is built to open standards: W3C ontologies (OWL, SKOS, SHACL), and aligned to the Open Semantic Interchange, the vendor-neutral semantic standard backed by Snowflake, Google, AWS, dbt and others. One definition of revenue, read the same way by your BI tools and your agents, on any platform.

Standards
  • dbt
  • PySpark
  • OWL
  • SKOS
  • SHACL
  • OSI-aligned
Platforms
  • Microsoft Fabric
  • Databricks
  • Snowflake
  • AWS
  • GCP

OSI is a young standard, finalized in early 2026. We build to it because vendor-neutral is the right long bet, and we’ll tell you where it’s mature and where it isn’t.

ontology/revenue.yaml
1concept: Revenue
2definition: Net amount invoiced to a customer.
3aligned_to: osi.metric.revenue
4source:
5  model: models/revenue.sql
6  grain: customer_id
7measures:
8  - name: revenue
9    expression: sum(net_amount)
10    unit: currency
11governance:
12  owner: finance.analytics
13  certified: true

one definition. read the same way by BI and agents.

What we don’t do

The fastest way to trust us is to hear where we stop.

  • We don't sell software licenses.

    The accelerators come with the engagement.

  • We don't rescue migrations with zero source documentation and no one who understands the legacy system.

    That's an archaeology project, and we're not the first call.

  • We don't do staff augmentation.

    You can't rent our people by the month to sit in your standups.

  • We don't replace your catalog or governance tool.

    We make Purview, Unity Catalog, and Collibra useful faster.

  • We don't lock you into a runtime you have to keep paying for.

    Open standards, your repo, your cloud account.

  • We don't leave your team dependent on us.

    Every engagement transfers the knowledge and the tools to run, extend, and own what we build, so your people are fluent in the modern data and AI stack after we go.

Trusted by enterprise data teams
7
named enterprise case studies
$500K+
annual savings, Heritage Grocers Group
60–90 days
typical delivery, not quarters
80%
migration time reduction with OpenOntos

Real modernizations, measured in weeks, dollars, and reporting latency.

See the case studiesSee the accelerator at openontos.ai
Case studies

Modernizations we've delivered.

The high-level results are here. The full story for each one lives on OpenOntos.

Featured by Microsoft
  • Microsoft customer story · microsoft.com

    Heritage Grocers Group saves $500,000 with Microsoft Fabric

    A Microsoft customer story: Heritage Grocers Group's modernization onto Fabric, built on Lucid Data Hub. $500K saved, 1.3TB of POS data, delivered in two months.

    Read the Microsoft customer story →
  • Microsoft Fabric Updates blog · coauthored with Microsoft

    Lucid Data Hub + Microsoft Fabric: Empowering Business Users with AI Agents

    Coauthored with Microsoft. Building and deploying AI agents directly on enterprise data in OneLake, with no code.

    Read on the Microsoft Fabric blog →
  • Microsoft Partner Spotlight

    AI agents on Microsoft Fabric for faster retail merchandising decisions

    Microsoft's Partner Spotlight series features our work building business-ready AI agents on Microsoft Fabric.

    Read on Microsoft Tech Community →
All case studies on openontos.ai →
About Lucid

A specialist firm, staffed by senior people.

Lucid Data & AI is a specialist enterprise data and AI consultancy. Engagements are staffed by senior engineers and architects who have delivered platform modernizations and AI foundations inside large, complex organizations. Senior practitioners, not a bench of juniors on a clock.

We work in small, focused teams and bring our own accelerators where they fit, so an experienced group does in weeks what larger firms staff for a year. We build to open standards, hand the knowledge and tooling to your team, and measure ourselves on outcomes that show up in weeks, dollars saved, and reporting latency.

The practice is led by founder Venu Amancha, who has spent his career in enterprise data engineering and platform modernization.

Featured

Lucid founder Venu Amancha on FabconTV: building enterprise AI agents.

Start with an assessment

Tell us what you're migrating off.

A fixed-fee assessment gives you a profile of your real data, the risks before cutover, and a plan. You keep the report whether or not you hire us for the build.

Book an assessment