Automation and Integrations
Move your data to a new system without losing a record
Switching systems is risky mostly because of the data. We clean it, map it, move it in rehearsed steps and prove every record arrived intact.
The problem
The problem this solves
New software is usually chosen for its features, but the project succeeds or fails on its data. Years of customer records, orders, cases and documents have to arrive in the new system complete, correct and linked together. If they do not, the team loses trust on day one and keeps the old system running "just in case".
Old data is always messier than expected. Duplicates, missing fields, free-text notes that hold important information, dates in five formats, codes that meant different things in different years. Some rules exist only in the old software's code or in someone's head.
A careful migration treats data as a project of its own. We profile the source data to see what is really there, agree mapping and cleanup rules with the people who know the business, write repeatable scripts rather than one-off manual fixes, and rehearse the migration on copies until the results match expectations. The final move happens in a planned window with a rollback plan.
Every migration ends with reconciliation: record counts, totals and spot checks compared between old and new. You see the evidence, not just a message that it is done.
Migrations are often part of a larger change, such as a new web application, a move to SaaS or a legacy modernization. In our booking system case study, one-way sync between old and new databases let the business keep taking bookings while the new system took over.
We keep your data safe throughout. Copies used for rehearsals are stored securely, sensitive fields can be masked for testing, and every copy is deleted when the project ends. Access to production data is limited to the few people who need it, and every access is logged.
What you get
What you get
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Data profiling
A report of what the source data contains, including gaps, duplicates and odd values.
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Mapping specification
Field-by-field mapping from old to new, with transformation rules.
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Cleanup rules
Deduplication, format fixes and enrichment agreed with your team.
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Repeatable scripts
Migration scripts that can be run and re-run safely.
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Rehearsals
Full test migrations on copies until results are right.
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Reconciliation report
Counts, totals and samples compared between old and new.
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Rollback plan
A tested way back if the final move goes wrong.
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Parallel running
Optional sync so old and new systems run side by side during the switch.
How we build it
How we build it
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1
Profile
We analyze the source data and document its real structure.
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2
Map and clean
Mapping and cleanup rules agreed with the data owners.
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3
Script
Migration scripts written and tested on samples.
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4
Rehearse
Full rehearsals on copies, with reconciliation each time.
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5
Cut over
Final migration in a planned window, then verification and sign-off.
AI and people
Where AI helps, where people decide
AI makes the repetitive parts faster. The decisions that shape your product stay with experienced people.
Where AI speeds things up
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Profiling large datasets and summarizing issues.
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Suggesting field mappings from sample records.
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Extracting structured data from free-text notes.
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Writing migration and reconciliation scripts.
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Finding likely duplicates for review.
Where people decide
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Which data moves, and which is archived or dropped.
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How duplicates are merged.
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What free-text data means.
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When rehearsal results are good enough.
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The timing of the final switch.
Is this right for you?
When this is the right choice
A good fit when
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You are replacing a CRM, ERP, database or custom system.
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You are moving from spreadsheets to proper software.
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Past migrations lost data or took far longer than planned.
Consider something else when
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The vendor offers a built-in import that covers your data well.
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You are starting fresh and do not need historical data.
Timeline and cost
What affects the timeline and cost
Small migrations of clean data can take days. Larger migrations with messy data, many linked tables or parallel running usually take several weeks, mostly spent on profiling, cleanup and rehearsals.
We do not publish fixed prices because scope drives cost. How we estimate.
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Data volume
More records mean longer rehearsals and checks.
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Data quality
Messy data needs more cleanup rules and review.
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Structural differences
Very different old and new models need complex mapping.
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Attachments
Documents and files add storage and linking work.
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Parallel running
Keeping systems in sync during the switch adds scope.
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Downtime limits
Near-zero downtime needs more planning and rehearsal.
Keep exploring
Related services, solutions and reading
Related services
View all related services- Automation and Integrations CRM, payment and API integrations, MCP connections and workflows that stop people copying data by hand.
- Web applications Custom browser-based software for the work your team does every day, from booking tools to operations systems.
- Database design Data models that stay fast and correct as your business grows, with backups and access rules in place.
- Legacy modernization Replace an aging system step by step, with the business running normally while the new parts take over.
- Multi-tenant architecture One product serving many customer accounts, with each account's data kept separate and secure.
- App backend and APIs The servers, databases and APIs behind your app: accounts, data sync, notifications and payments.
Solutions
View all solutions- AI automation and integrations Connect the tools you already use so data moves once, correctly, and AI reads the parts that arrive as text or documents.
- Compliance tracking Track filing deadlines, licenses and obligations in one place, with reminders and AI summaries of relevant rule changes.
- Automated reporting Reports that build themselves from your systems on schedule, with a plain-language summary of what changed.
- Forecasting dashboards Forecast demand from your sales history and seasonality, and flag items to reorder before they run out.
- Payroll automation Turn pay rules, attendance and leave into tested software so the monthly run becomes a review and payslips go out in one batch.
Industries
View all industries- Finance and accounting Client portals, document collection, invoice and receipt processing, and reporting for accounting firms and finance teams.
- Healthcare Patient booking, intake forms, internal knowledge assistants and admin automation for clinics and care providers.
- Property management Tenant portals, maintenance tracking, owner statements and inbox automation for property managers.
Case studies
View all case studies- Beach rental booking system A long-running booking system kept earning while it was extended, tested and given a safe, gradual path to a modern stack.
- Vacation rental operations system One calendar per property, cleaning tasks created from bookings, and owner statements built from the same records.
- Facility management platform Every request, inspection and cost for every building now lives in one place, with a clear owner and status.
- Tax filing service platform Clients upload documents and see where their case stands, and staff work from one queue with deadlines and reminders.
- Fashion deal aggregator Sale items from more than thirty brands are collected every night and classified consistently by a three-stage pipeline.
Guides and articles
View all guides and articles- How to migrate a legacy application Replace an old system step by step without stopping the business, with tests, data checks and a way back.
- PostgreSQL vs MySQL The two most popular open-source databases compared for business applications.
- How we review AI-generated code The checks we apply to every AI-assisted change, from tests and security to readability and business rules.
MCP servers
View all mcp servers- Databases Servers that let AI query and, where allowed, change data in SQL, NoSQL, analytics and vector databases.
- Airtable Airtable's official hosted MCP server lets AI list workspaces, create bases and read records, with wider read and write access to tables and automations.
- Azure Microsoft's Azure MCP server lets AI work with many Azure services, from storage and Key Vault to SQL, monitoring and AKS, with a read-only mode.
- BigQuery Google's remote BigQuery MCP server lets AI list datasets and tables and run SQL, with a read-only query tool and IAM controls.
- Chroma Chroma's official MCP server lets AI create collections, add, query, update and delete documents in a Chroma vector database, local or cloud.
- Elasticsearch Elastic's Agent Builder MCP endpoint lets AI search indices, run ES|QL queries and read mappings and documents in Elasticsearch.
Glossary terms
View all glossary terms- Database A database is an organized store of data that software can search, update and keep consistent, such as customers, orders or bookings.
- JSON JSON is a simple text format for structured data, made of names and values, that most software and APIs use to exchange information.
- Technical debt Technical debt is the future cost of shortcuts taken in software, such as quick fixes or missing tests, which make later changes slower and riskier.
- API An API, or application programming interface, is a defined way for one piece of software to request data or actions from another.
- Deployment Deployment is the process of releasing a new version of software to the servers where users can reach it.
- Function calling Function calling is a model feature that lets an AI model request a specific function, with structured inputs, for the application to run.
FAQ
Questions about data migration
Have a question that is not here? Ask us directly.
Through reconciliation: record counts, financial totals and random samples compared between old and new systems, with a signed-off report.
Yes. We plan the final switch for a quiet window, and for critical systems we can run old and new side by side with sync until you are confident.
We agree what to archive, what to delete and what to move, following your legal retention rules.
Yes, and it is a common starting point for new internal tools. See internal tools and database design.