Legacy data workflow migration
Move your legacy data workflows. Prove every number matches.
PRAX-IA Bridge migrates the desktop data-prep and analytics workflows your analysts built over the years to Microsoft Fabric, Databricks, Snowflake, AWS or Google Cloud, and reconciles every output against the original before anything is switched off.
- Fixed-price assessment to start
- Row-by-row reconciliation
- A person signs off every cutover
- Your cloud, your code, your data
- Read files
- Filter
- Join
- Formula
- Summarize
- Write output
read sources → 5 tables✓filter status = "posted"✓join ledger ⋈ bank on ref_id✓derive net = gross − returns✓group by account, month✓write reconciled_balances✓
Versioned · reviewed · ready to reconcile
Data and AI consulting
AI moves the process. Your people sign off.
AI agents, data platform modernization and data and AI governance that reach production, on Microsoft Fabric, Databricks, Snowflake, AWS or Google Cloud. With the cost clear before we start, tested before release and a person approving what matters.
- You know the cost before you start
- Tested before it reaches production
- A person approves what matters
- Every execution is recorded
Tap a person or an agent to see their part
9 agents execute, 5 people decide
Every agent has a clear job, records every action with its author and date, and works from a single screen. Tap anyone to isolate their path and see what they do, who they work with, who they wait on and what they depend on.
- Preview before the go-ahead. Every action is previewed in full; it is only committed once a person authorizes it.
- Cited or proposed. Anything without backing in the case file is flagged, not asserted.
- Nothing is deleted. Versions are superseded and the history stays.
- Doing it twice duplicates nothing.
Process owner
Declares the process, its scope and its date; signs off on the sizing and the plan.
- Works with
- Decides
- scope, priority and dates
- How it iterates
- Every scope change comes back to them: no one decides it behind their back.
Classifier agent
Classifies every input that comes in: what it is and which step it belongs to.
- Works with
- Waits on
- How it iterates
- Proposes once, with a confidence level; the person confirms on upload. Anything doubtful goes to an inbox; nothing is guessed.
- What it costs
- minimal tokens · it proposes and a person confirms
Estimator agent
Sizes the work with the evidence in the case file and sends it for sign-off.
- Works with
- Waits on
- Depends on
- How it iterates
- Won’t size again while a sign-off is pending; once the sign-off is recorded, it seeds the plan.
- What it costs
- metered tokens · cost on screen before the go-ahead
Requesting team
Provides the inputs that exist and answers what is missing.
- Works with
- Decides
- which inputs to hand over and when
- How it iterates
- Whatever goes unanswered stays as a visible gap, with a date and a name.
Inquiry agent
Builds the questionnaire for what is missing, sends it and brings the answers back into the case file.
- Works with
- Waits on
- Depends on
- How it iterates
- Send, wait, collect. Prioritizes by risk: the wait is over when no high-risk question remains.
- What it costs
- metered tokens · cost on screen before the go-ahead
Reader agent
Reads the raw artifacts (models, contracts, code, spreadsheets) and captures the current state without calling an AI model.
- Works with
- Depends on
- How it iterates
- Reads again whenever a new artifact arrives. Whatever it can’t back up, it flags as missing, with the reason.
- What it costs
- zero tokens · it is arithmetic or integration
Measuring agent
Measures how much of the deliverable can already be written, section by section, and turns the gaps into concrete requests.
- Works with
- Depends on
- How it iterates
- Measures again with every input that arrives: it is the case file’s thermometer. Same case file, same number, every time.
- What it costs
- zero tokens · it is arithmetic or integration
Technical lead
Approves the threshold, validates the diagrams and signs off on what was written.
- Works with
- Decides
- whether to write below the threshold and which diagram holds
- How it iterates
- Sends it back with a note; a return without a reason doesn’t exist.
Diagram agent
Proposes the diagrams before a single word of prose is written; they are reviewed and validated on screen.
- Works with
- Waits on
- Depends on
- How it iterates
- Works with the technical lead until it is validated. Without human validation, it doesn’t go into the document.
- What it costs
- minimal tokens · it proposes and a person confirms
Writer agent
Writes the deliverable in the official template, citing the case file and embedding the validated diagrams.
- Works with
- Waits on
- Depends on
- How it iterates
- Reviewer sends it back → new version. The previous one is superseded, not lost. Cost estimated before the go-ahead.
- What it costs
- metered tokens · cost on screen before the go-ahead
Role-based reviewer
Reviews what was written, with the citation next to every claim, and signs off or sends it back.
- Works with
- Decides
- whether the deliverable passes or goes back, and why
- How it iterates
- Their sign-off expires if the version changes.
Supervisor agent
Sweeps the board every day and knocks on the door only when a task truly puts the date at risk.
- Works with
- How it iterates
- Every day at the same time, with the same criteria. One alert per person per day, not repeated until something changes. It never measures people: it measures contradictions.
- What it costs
- zero tokens · it is arithmetic or integration
Auditor agent
Goes through the deliverables, chases what is missing and makes sure a fix doesn’t break a rule in force.
- Works with
- Depends on
- How it iterates
- Every return fixes the document or fixes the rule. By the third round, the mistake is no longer made on any project.
- What it costs
- minimal tokens · it proposes and a person confirms
Certifying committee
Certifies with the evidence in front of it. Its signature closes the process.
- Works with
- Waits on
- Decides
- the certification, with authority
- How it iterates
- A sign-off on an earlier version says nothing about the current one.
- Process owner, person: Declare, Assign. Works with Estimator agent, Supervisor agent, Technical lead.
- Classifier agent, agent: Declare, Map. Works with Reader agent, Estimator agent.
- Estimator agent, agent: Declare, Measure. Works with Classifier agent, Inquiry agent, Supervisor agent.
- Requesting team, person: Declare, Map. Works with Classifier agent, Inquiry agent.
- Inquiry agent, agent: Map, Measure. Works with Estimator agent, Measuring agent.
- Reader agent, agent: Map, Measure. Works with Measuring agent, Diagram agent, Writer agent.
- Measuring agent, agent: Measure, Assign. Works with Reader agent, Inquiry agent, Diagram agent, Writer agent.
- Technical lead, person: Measure, Assign. Works with Measuring agent, Diagram agent, Writer agent.
- Diagram agent, agent: Measure, Assign. Works with Measuring agent, Writer agent, Reader agent.
- Writer agent, agent: Assign, Record. Works with Measuring agent, Diagram agent, Reader agent.
- Role-based reviewer, person: Assign, Record. Works with Writer agent, Auditor agent.
- Supervisor agent, agent: Assign, Record. Works with Auditor agent, Estimator agent.
- Auditor agent, agent: Assign, Record. Works with Writer agent, Supervisor agent, Role-based reviewer.
- Certifying committee, person: Record. Works with Auditor agent, Role-based reviewer.
See it operate
A complete process, from clock-in to sign-off.
A real process, end to end: people clock in at the plant, on the road and at the office; AI reconciles, rebuilds what is missing and prepares the evidence; HR reviews and signs off.
Built on Medular, our data infrastructure for operated processes. Sample data; the on-screen labels are in Spanish.
Products running, not slides
This already exists. See how it works.
Real screens from our products, recorded with sample data. No mockups.
- Data across three clouds, live
- A migrated workflow, running
- Data domains and their owners
- Agents code; a senior validates
- A person approves, then it’s published
Data and AI governance
AI asks for permission too.
With PRAX-IA Quórum, every AI agent is one more user of your data governance: it has a role and an owner, requests access to the table it needs like a person does, the data owner approves with an expiry date, and every query is logged.
- Each person with their agent, without widening access
- Permissions that expire on their own
- One governance layer across AWS, Azure and Google Cloud
- A log of every AI query
- AI Ana’s agent requests accessTable sales_by_store · read only · 30 daysReason: reconcile the October close
- Q Quórum checks the requestRole: analysis agent · owner: Ana · sensitive data: noAna can see this table: the agent inherits, it never widens
- P The data owner approvesLaura, Finance · approved until November 25Sensitive data would require a second sign-off
- ✓ Access granted, and scopedThat table only, read only, expires on its ownNo service accounts that see everything
- ≡ Every query is logged10:42 · Ana’s agent · sales_by_store · 1,284 rowsWho asked, which agent, what data and who approved it
What we do
We close the gap between business and technology.
From getting the data in order to operating complete processes with AI, on the cloud and tools you already use.
Governed data the business can use
Data and AI governance for people and agents, modern platforms and migrations without stopping operations.
AI that works in your operation
Agents that answer and resolve, documents that read themselves and data that answers in plain language.
From idea to production
Knowing where to start, testing before release and measuring what each model costs.
Complete processes, operated with AI
PRAX-IA Opera: we operate a complete stage of your process, with an SLA and your people approving what matters.
Meet PRAX-IA Opera →PRAX-IA Bridge · the difference
If a number doesn’t match, you see it before cutover.
Every migrated workflow runs on the same inputs as the original. Rows, columns, totals and checksums are compared, and you get the reconciliation report before anything is switched off.
- InventoryWe read your legacy workflows and measure how many there are, what they touch and which ones matter.
- ConvertAI rebuilds each workflow as clear, versioned steps on the platform you choose.
- ReconcileSame inputs, same outputs: rows, columns, totals and checksums compared against the original.
- Cut overA person signs off, reports are reconnected and the old workflow is retired.
1,284,3101,284,310✓ match3838✓ match48,920,113.0748,920,113.07✓ match6,4126,412✓ matcha91f…7c2ea91f…7c2e✓ matchHow to start
A fixed price first. Then batches you can count.
Target platforms: Microsoft Fabric, Databricks, Snowflake, AWS or Google Cloud.
Fixed-price assessment
An inventory of your legacy workflows plus a handful migrated and reconciled end to end on your target platform. You see the method working on your own data before committing to more.
- Workflow inventory with priority and complexity
- A few workflows migrated and reconciled
- Target platform recommendation with running cost
- A batch plan for the rest
Migration in batches
The rest of the workflows move in batches, each one reconciled and signed off before the next. You always know what is done, what is next and what it costs.
- Fixed scope per batch
- Reconciliation report per workflow
- Old and new run in parallel until sign-off
- Your team trained to own the result
What we’ve put into operation
Implementations, not promises.
Some of the projects we’ve taken to production, described by industry under non-disclosure agreements. The screens are from the product, with sample data.
- Enterprise · finance
Legacy workflows migrated to Microsoft Fabric
Reconciliation workflows rebuilt in Fabric, compared against the original; the team now requests changes in plain words.
- Microsoft Fabric
- OneLake
- Power BI
- Enterprise · data
Legacy workflow modernization on AWS
Inherited data processes moved to an AWS platform, with lineage traced end to end and gaps declared, not assumed.
- AWS Glue
- Step Functions
- Amazon Redshift
- Airline · sample scenario
Data and AI governance across three clouds
PRAX-IA Quórum: people and AI agents under one governance layer; every agent requests access like a person and every query across AWS, Google Cloud and Azure is logged.
- Microsoft Fabric
- Microsoft Purview
- BigQuery
- Databricks
- Multi-site operation
Workforce time tracking, from clock-in to payroll
Medular reconciles clock-ins from plant, road and office, rebuilds what is missing and HR reviews and signs off.
- Medular
- Agents
- Evidence per person
- Software team
Software delivery with agents and senior review
Agents write and test code; a senior developer validates what was written and nothing reaches production without their sign-off.
- Agents
- Continuous integration
- Senior review
- National retail
Solution architecture certification
Teams and partners work on one process: agents assemble the file and the people who decide sign off.
- Agents
- Official templates
- Decision log
Sound familiar?
Repetitive work has a name. And an agent that does it.
If it’s repetitive, high-volume and its result can be checked, an agent can do it. Your people keep what needs judgment.
- Finance
«Every invoice gets keyed in by hand, one by one.»
An agent reads the invoice, checks it against the purchase order and only passes you the ones that don’t match.
- Collections
«Payment reminders depend on someone having time to call.»
A voice agent calls, reminds, records the promise to pay and escalates what’s unclear.
- Data
«Nobody trusts the number in Monday’s report.»
Every figure comes from governed data, with its formula and lineage in plain sight.
- Technology
«Migrating the legacy workflows feels like a two-year project.»
AI rebuilds each workflow on the platform you choose and proves the output matches the original.
- IT service desk
«First-level tickets pile up all week.»
An agent classifies, solves the repeat ones and routes the rest to the right person.
- Service
«Appointments are missed because nobody confirms them.»
An agent confirms by WhatsApp or by phone, reschedules and frees up the slots.
AI maturity
Which step is your company on?
From the isolated pilot to the process that runs itself. Tap a level.
Level 1 · Explore. Scattered pilots, no governed data.
Next step: Pick the first use case that pays and measure where it starts.
Find out your level →Level 2 · Found. Data platform and governance in place.
Next step: Get reliable data so AI isn’t building on sand.
Find out your level →Level 3 · Produce. Use cases in production, measured.
Next step: Test benches, cost per execution and a person approving.
Find out your level →Level 4 · Scale. Agents with tests, controls and measured cost.
Next step: Reuse what already works in more areas without multiplying risk.
Find out your level →Level 5 · Operate processes. Complete processes with an SLA and a person who approves.
Next step: PRAX-IA Opera: we operate the whole stage and answer for the result.
Find out your level →PRAX-IA Opera
Complete business processes, operated with AI and an SLA.
We don’t sell standalone agents: we operate a complete stage of your process. Agents do the work, your people approve at the points that matter, and we answer for the result, not for hours.
Also migrating
Legacy BI to Power BI
BusinessObjects, OBIEE and Qlik environments are facing end-of-support timelines. We move reports and semantic models to Power BI and check that every figure matches the original before users switch.
- Report and universe inventory
- Semantic models rebuilt in Power BI
- Figure-by-figure comparison
- Users switched in waves
Why so many AI projects stall
The problem is rarely the model.
It’s the cost nobody measured, the value nobody fixed and the control nobody designed. Exactly what we solve before building.
Why PRAX-IA
A senior team in your time zone.
- Your working hoursOur team is in Mexico City: the same time as US Central in winter and one hour behind in summer. Your stand-ups happen in your morning, not overnight.
- Senior architects, nearbySolution architects certified on the major clouds, with two decades inside large companies. Close enough to fly in when it matters.
- Less than a local agencyNearshore rates for senior work, with a fixed price for the assessment and a fixed scope for every batch.
- Proof, not promisesEvery migrated workflow comes with its reconciliation report. If a number doesn’t match, you see it before cutover, not after.
FAQ
Before you start.
Which legacy tools can you migrate from?
Desktop data-prep and analytics workflows, spreadsheet-based processes, legacy ETL and legacy BI. The assessment confirms what you have and how each workflow will move.
Which platforms can you migrate to?
Microsoft Fabric, Databricks, Snowflake, AWS and Google Cloud. The target is chosen by your IT strategy, your existing cloud and the team that will run it.
How do we know the numbers match?
Each migrated workflow runs on the same inputs as the original, and outputs are compared row by row: counts, columns, totals and checksums. You receive the reconciliation report before anything is switched off.
What does the fixed-price assessment include?
An inventory of your workflows, a handful of them migrated and reconciled end to end on your target platform, a platform recommendation with running cost, and a batch plan for the rest.
Do we have to stop operations?
No. New and old workflows run in parallel until the reconciliation proves they match. Only then do you switch.
Where is your team?
In Mexico City, working US hours. We travel for workshops and kickoffs when needed.
Fixed-price assessment
Tell us what you need to move.
We reply within one business day. If your case isn’t a fit, we’ll say so in that first email.
- Fixed-price assessment to start
- Row-by-row reconciliation
- A person signs off every cutover