Now booking new engagements

Data engineering. Machine learning. AI agents in production.

Quasar IT Services is a senior-only engineering team for three things that only work when they work together: the data platform underneath, the model lifecycle on top of it, and the AI agents your business actually asked for. Built to survive production — and handed over to run without us.

Book a free architecture review See the 10-day assessment

10 days: From kickoff to a costed, sequenced delivery roadmap
100%: Senior engineers. No pyramid, no bench, no juniors billed as architects
Fixed fee: Assessments and POCs are priced up front, not by the hour

Databricks Snowflake Microsoft Fabric Unity Catalog Delta Lake Lakeflow Apache Spark Structured Streaming Apache Kafka Confluent Debezium CDC MLflow Vector Search Model Serving Asset Bundles Terraform dbt Azure AWS Power BI

Why people call us

Nobody wakes up wanting a data consultancy.

They wake up with a bill that doubled, a report nobody trusts, a model that never left a notebook, or an AI pilot that cannot reach its own data. If one of these is a sentence someone says in your organization, we can probably help.

Our Snowflake bill went from $600K to $2.1M and nobody can explain why.

We profile the warehouses, find the workloads that are actually driving spend, and model what they cost on Databricks SQL and serverless jobs before you commit to anything.

Cost

The nightly batch takes 11 hours and finishes after the business needs it.

Usually a rewrite of the worst 10% of the pipeline plus incremental processing on Delta. We find the critical path first, then fix it, rather than lifting and shifting the mess.

Latency

Our Oracle estate is the bottleneck for everything and the licenses renew soon.

Log-based CDC off Oracle into Kafka, landed on Delta, with the business logic rebuilt as testable transformations. The source stays live until you decide to switch it off.

Migration

We bought Databricks and now nobody knows how to architect it properly.

Our most common engagement. Unity Catalog design, workspace and network topology, cluster policies, cost guardrails, CI/CD, DR. We leave you with a platform your team can actually run.

Platform

The AI initiative stalled because the data is ungoverned and nobody trusts it.

Governance is the AI project. We land lineage, permissions and quality in Unity Catalog first, then build retrieval and agents on top of something defensible.

Enterprise AI

We have forty notebooks, two data scientists and no models in production.

The gap is almost never the modelling. It is the lifecycle: tracked experiments, a registry that gates promotion, reproducible features, and serving with monitoring attached. We build that path once and your team ships on it after.

Machine learning

What we do

Three capabilities, deliberately narrow.

We do data engineering, machine learning and AI agents — on Databricks, Snowflake and Microsoft Fabric, with Power BI on top. That is the whole list. Everything we sell stacks on everything else, which is why the pieces fit together instead of fighting.

01 / Data engineering

The platform everything else stands on

Ingestion, modelling and governance done properly on Databricks, Snowflake or Microsoft Fabric, with Power BI semantic models on top. The goal is a number the business trusts and a pipeline your team can change without fear.

Batch, streaming and CDC ingestion
Lakehouse and warehouse modelling
Governance, lineage and access control
Power BI semantic layer and certified datasets

Data engineering

02 / Machine learning

Models that reach production, and stay there

The unglamorous lifecycle that decides whether a model is an asset or a demo: tracked experiments, reproducible features, a registry that gates promotion, and serving with monitoring wired in from day one.

MLflow tracking, registry and model governance
Feature pipelines that are reproducible
Evaluation and regression gates before promotion
Serving, drift monitoring and retraining paths

Machine learning

03 / AI agents

Agents with tools, evidence and limits

Retrieval over your real documents and tables, tools that are governed functions rather than open query surfaces, evaluation you could show an auditor, and a loop with a budget that provably terminates.

Retrieval over governed, permissioned sources
Narrow tool contracts, not open SQL access
Evaluation sets and regression gates
Human checkpoints before consequential writes

AI agents

The reference architecture

One path from raw source to working agent.

This is the shape of most of our engagements. We rarely build all of it at once — we build the slice that unblocks the business, then extend it.

Source

Operational systems

Oracle / SQL Server
SAP / ERP / CRM
Files, APIs, events
Legacy warehouses

Capture

CDC & ingestion

Log-based CDC
Kafka / Confluent
Auto Loader
Batch extracts

Platform

Lakehouse or warehouse

Databricks / Snowflake
Microsoft Fabric
Bronze / Silver / Gold
Governance & lineage

Consume

Analytics, ML & agents

Power BI / SQL
Models & serving
Retrieval & RAG
Agents & tools

Bronze

Raw, append-only, exactly as the source produced it. Schema evolution handled, nothing silently dropped, full replay possible.

Silver

Conformed, deduplicated, history tracked properly with SCD where the business actually needs it. This is where data quality is enforced.

Gold

Business-facing marts and metrics with agreed definitions, plus the governed surface that BI and AI both read from.

How to start

A ladder, not a leap.

You should not have to sign a six-figure statement of work to find out whether we are any good. Each step is useful on its own and each one earns the next.

Step 01

Architecture review

Sixty minutes with a senior architect. You bring the current architecture and the things that hurt; you leave with a target-state diagram and the three changes worth making first.

No charge

60 minutes

Data & AI Assessment

Our flagship fixed-fee engagement. We profile what you actually have, design the target architecture, model the cost, quantify the risk, and hand over an implementation plan your finance team can approve.

$14,400 - $24,000

10 business days

Step 03

Proof of concept

One real workload, end to end, in production-shaped code. Not a notebook demo — a genuine slice with tests, governance and reconciliation against the source system.

$36,000 - $60,000

4 - 6 weeks

Step 04

Production build

The full migration or platform build, delivered in increments with your engineers working alongside ours. The goal is a team that no longer needs us.

$120,000 - $400,000+

3 - 9 months

Start with the assessment Full pricing & rates

Reusable IP

We bring accelerators, not just people.

Every engagement feeds our internal toolkit, and every client gets the toolkit. It is the reason a small team can deliver work that normally needs a much larger one — and the reason we can quote fixed fees with a straight face.

Migration Factory — profiling, SQL conversion, reconciliation harness
Streaming Blueprint — CDC to Bronze to SCD2 with replay and DQ built in
Governance Baseline — Unity Catalog, groups and permissions as Terraform
Agent Foundation — retrieval, tools, evaluation and serving scaffolding

How the accelerators work

reconcile.py — parallel-run validation (illustrative)

# Prove equivalence before anyone signs off a cutover.
from quasar.migrate import Reconciler

recon = Reconciler(
    source  = "snowflake://prod/finance.fact_orders",
    target  = "uc://prod.finance.fact_orders",
    keys    = ["order_id"],
    # tolerate float drift, never tolerate row drift
    numeric_tolerance = 1e-9,
)

report = recon.run(partition_by="order_date")

report.assert_no_missing_rows()
report.assert_column_parity(threshold=1.0)
report.publish("prod.audit.migration_evidence")

# example output: 41,882,113 rows compared - 0 mismatches - 6m 12s

How we work

Boring, sequenced, evidence-driven.

Migrations fail politically far more often than technically. Most of our process exists to make the technical risk visible early, so the organization keeps its nerve.

Fixed-fee phases Weekly demos Your repo, your cloud No lock-in

Understand the estate

Query logs, job schedules, lineage, volumes and spend. We want the real workload profile, not the architecture diagram someone drew two years ago.

Days 1–4

Design the target

Unity Catalog model, compute topology, ingestion pattern, security boundary and cost envelope. Reviewed with your architects, not presented at them.

Days 5–8

Prove the risky slice

We build the workload everyone is most worried about first, and reconcile it against the source. Confidence comes from evidence, not from the plan.

Weeks 3–8

Industrialize

Convert the long tail with the accelerators, wrap it in CI/CD and monitoring, and run in parallel until the numbers match for long enough to be boring.

Months 2–6

Hand over properly

Runbooks, architecture decision records, paired delivery and training. We would rather lose the follow-on work than leave you dependent on us.

Continuous

Why Quasar

A quasar is what happens when accumulated mass finally gets organized.

Matter spirals into a supermassive black hole, and the disk around it becomes the brightest thing in the observable universe — brighter than the entire galaxy it sits in. It is not new material. It is material that was always there, finally arranged so its energy can escape.

That is the entire pitch. Your organization is already sitting on the mass. We are in the business of the arrangement.

Big firms sell you a pyramid. We sell you the senior engineers who will actually be in the repo.

Senior only. Everyone who bills on your project has run a migration before.
Fixed fee where it counts. Assessments and POCs are scoped and priced up front.
Your infrastructure. Code lands in your repos, in your cloud, under your control.
Honest scoping. If a migration is a bad idea this year, we will say so in writing.

More about how we operate

Start here

A free 60-minute architecture review

Bring your current architecture, data volumes and the workloads that hurt. You leave with a target-state diagram on Databricks, the three changes that would cut the most cost or latency, and an honest answer on whether you need us at all.

Book the review See the paid assessment

No slide deck. No procurement process. One senior architect on the call.