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One firm for the data and AI work, not four vendors

Most companies end up with a platform team, a BI vendor, an AI pilot that never shipped, and nobody accountable for the join between them. We are set up to be the single point of contact for all of it. Where we have a gap, we hire for it rather than sell around it.

Describe your project

A senior engineer reads it and replies within one business day. Or write to emilio@lyticaconsulting.com.

Core services

The four services below have their own pages because they are where most engagements start. The rest are real work with real deliverables — they just tend to arrive as part of something larger.

Data engineering

You end up with a data platform your team can run without us: modeled, tested, monitored, documented, and deployed through a pipeline anyone on the team can read.

  • Infrastructure defined in codeWarehouse, storage, compute, networking, and access, on AWS or Azure, written down so the platform can be rebuilt rather than remembered.
  • A modeled warehouseFacts and dimensions designed around the questions your business actually asks, rather than a mirror of whatever your source systems happen to emit.
  • Ingestion and transformationETL and ELT in Python and SQL, orchestrated in Airflow, with retries, backfills, and dependencies that are explicit instead of implied.

AI and machine learning

You end up with business users asking questions in plain language and getting answers that are right — because the model queries a governed set of metric definitions instead of guessing at your raw tables.

  • Conversational analyticsA question interface over a semantic layer where every metric has one tested definition. The model composes answers from defined metrics; it does not invent SQL against raw tables.
  • Agentic workflowsAgents that do real work inside your systems — run a process, prepare a report, open and route a ticket — with the boundaries of what they may touch written down.
  • Coding agents grounded in your codebaseAgents that know your repository layout, your standards, and your deployment path, so what they produce reads like your code and not like a generic sample.

Analytics and decision products

You end up with the metrics, analysis, and data products that turn a platform into decisions — delivered into whatever BI tool you already run.

  • Metric definitionsOne agreed, tested definition per metric, versioned in your repository, so the same question returns the same number in every tool that asks it.
  • Analysis that answers a specific questionA written answer with the working shown, not a dashboard handed over for someone else to interpret.
  • ExperimentationDesign, sizing, and readout for tests, so a result means something and a flat result is recognised as flat rather than re-cut until it moves.

Data and AI strategy assessment

A fixed-scope engagement, two to four weeks depending on how much there is to read, that ends with a written architecture review, a prioritized roadmap, and a cost baseline. It is deliberately useful on its own — you could hand it to another firm and they could act on it.

  • A current-state architecture reviewWhat you have, how it fits together, where it will break next, and which parts are load-bearing without anyone realising.
  • A prioritized roadmapSequenced work with effort estimates and dependencies, ordered by what unblocks the most people rather than what is most interesting.
  • A cost baselineWhat your platform costs to run now, which line items are growing fastest, and what is available to recover.
Describe your project

A senior engineer reads it and replies within one business day. Or write to emilio@lyticaconsulting.com.

Two ways to start small

Neither of these needs you to trust us with a platform rebuild first.

Platform cost optimization

We read your warehouse and cloud bill, find what you are paying for and not using, and tell you what to change and what it saves. The finding is measurable, which means so are we.

Describe your platformWhat the review includes

The supporting work

Real deliverables, not filler. These usually arrive as part of a larger engagement, but any of them can be bought on its own.

Semantic layer and metric definitions

One tested definition of every metric that matters, versioned in your repository, that a person or a language model can be trusted to query. This is the layer that makes conversational analytics trustworthy rather than a demo, and it is worth having even if you never build the chat interface.

  • A metric catalogue with one owner and one definition per metric
  • Tests that run on every change, so a redefinition cannot land silently
  • Version history, so "when did this number change?" has an answer

Data governance, quality, and observability

Lineage, data contracts, quality monitoring, and access control — enough to know where a number came from, who can see it, and whether it is currently wrong. Sized to what you actually need, not to a framework.

  • Column-level lineage from source to report
  • Contracts between producing and consuming teams, with tests attached
  • Freshness, volume, and distribution monitoring with real alert routing
  • Access control mapped to roles rather than to individuals

Platform cost optimization

Warehouse and cloud spend reduction. We read the bill and the query history, find the compute that runs for nobody and the storage nobody reads, and rank the changes by what they save against what they cost to make. You get the list whether or not you hire us to act on it.

  • A line-by-line read of where the money goes
  • A ranked list of changes with the saving and the effort for each
  • The changes themselves, if you want us to make them

Fractional data leadership

A senior data leader embedded part-time, for organizations that need the judgment but not yet the full-time hire. Architecture decisions, vendor selection, hiring, and roadmap — including telling you when the answer is to hire someone permanent.

  • A set number of days per month, with a named person
  • Architecture and vendor decisions made and documented
  • Interview loops designed, and candidates assessed technically

Migration and modernization

Moving a legacy warehouse or pipeline estate to a modern stack, with a cutover plan that does not depend on a weekend going perfectly. Old and new run in parallel and get compared on real data until the numbers agree.

  • A migration plan with a rollback path at every stage
  • Parallel running with automated reconciliation between old and new
  • Decommissioning of the old system, which is the step most migrations skip
Describe your project

A senior engineer reads it and replies within one business day. Or write to emilio@lyticaconsulting.com.

What we work in

Only things we run in production. A short honest list is worth more than a long one that fails the first technical question.

Cloud
AWS, Azure
Warehouse and compute
Snowflake, Databricks, Synapse, Spark
Movement and orchestration
Airflow, Kafka, Flink, Data Factory, Glue
Languages
Python, SQL, Java
Delivery
Docker, Git, CI/CD, FastAPI

What we do not do

It is faster for both of us if you know this now.

  • Staff augmentation by the headcount. You are not renting seats, and we do not bill for a bench.
  • BI tool implementation. If you need a Power BI or Tableau practice, we are the wrong firm and we will say so on the first call.
  • Six-month discovery phases. If we cannot put working software in front of you inside a month, the scope is wrong.
  • Work we cannot staff with senior people. We would rather turn it down and point you somewhere else than learn on your project.
Describe your project

A senior engineer reads it and replies within one business day. Or write to emilio@lyticaconsulting.com.

How you would contract with us

Four shapes. Procurement usually wants to know how it ends, so that column is there too.

ModelForHow it is scopedHow it ends
Fixed-scope projectA known outcome with a defined edgeStatement of work and fixed price, agreed before we startOn delivery and handover
Dedicated podOngoing build where the roadmap will changeMonthly, by team composition30 days' notice
AssessmentA first engagement, or a second opinionFixed scope and fixed price, two to four weeksWhen the roadmap is in your hands
Fractional leadershipSenior data judgment without a full-time hireDays per month30 days' notice

Tell us what you are trying to build

Describe the situation in your own words. A senior engineer reads it and replies, usually with questions rather than a brochure.

Describe your project