Data integration services
A migration doesn't fail loudly. It fails quietly: a field mapped to the wrong column, a duplicate that slipped through, a sync job that has been dropping updates for three weeks before anyone notices the numbers don't add up. Clay TechSystems builds data integration solutions and database migration services where the validation layer isn't a manual spot-check at the end. It is a system built to flag the row that doesn't belong, before it ever reaches production.
The quiet part
Every migration project has a moment where the dashboard says complete and the data still isn't trustworthy. Three patterns cause almost all of it. None of them show up in a demo. They show up three months after go-live, when a finance team asks why two reports don't match.
01
A field with no mapping rule doesn't throw an error. It just doesn't show up on the other side. Nobody notices until a report comes back wrong months later. AI watches for the column that left the source and never arrived, instead of waiting for a human to notice a blank.
02
Two systems running in parallel during a migration window will disagree unless something is actively reconciling them in real time, not just at cutover. An AI reconciliation pass compares both sides as they move, and flags the record that has started living two lives.
03
A field can be the right type and the right length and still be wrong. A transaction amount that is technically valid but statistically impossible for that account needs a different check than "is this a number." That is the check AI is built to make.
The pipeline
Every stage below is a discipline Clay already delivers. What has changed is the check that runs while the stage is running.
01
Pulling data out of wherever it currently lives: databases, flat files, APIs, legacy systems, in whatever format it is actually in (CSV, Excel, JSON, XML), including large volumes and encrypted, access-controlled transfer where the source demands it. AI profiles the extract as it lands, so a truncated file or a schema that shifted overnight is caught before anyone starts mapping it.
02
Before anything moves, it gets scrubbed: duplicate removal, format and type correction, null and missing-value handling, and enrichment against external sources where the raw data is incomplete on its own. AI proposes the likely correction and the confidence behind it, so a human approves the ambiguous row instead of guessing through thousands of them.
03
Source structure to target structure, field by field: one-to-one, one-to-many, or many-to-one, including the relationships a lookup table cannot handle, with custom transformation logic where built-in functions are not enough. AI-assisted schema matching drafts the first map. Unmapped fields are flagged, not dropped.
04
Cleansed, mapped data moves into its destination with incremental or full loads, and real-time streams where the use case demands it, with validation before load and a full log of what happened and what did not. AI scores each batch against the pattern of batches that loaded cleanly, and holds the one that doesn't fit.
05
Standardising structure across sources so five systems of inconsistent naming and formatting become one coherent model: fewer duplicate fields, less complexity, and data that is actually analysable once it lands. AI clusters near-duplicate labels ("cust_id", "customerId", "CID") into one field before they become three columns in the warehouse.
06
Once systems live in parallel, they have to stay in agreement: periodic or real-time updates, so the old system and the new system do not quietly drift into two versions of the truth. AI watches the delta. A sync job that has been dropping updates for three weeks gets flagged in the week it starts, not in the quarter someone notices the numbers.
Where it lands
Extraction and mapping only matter if the destination can carry the load. Clay's data integration services run on infrastructure built for that.
Already part of Clay's active data warehousing stack. The target when a migration needs to land somewhere built for elastic, cloud-native scale, rather than a fixed-capacity server that becomes next year's bottleneck. AI workloads on Snowflake only stay honest if the rows that landed were flagged before they got there.
Already running underneath
The two cloud platforms already running Clay's integration and migration work today: compute, storage, and networking that the pipeline itself depends on.
Actively expanding
Clay is extending the same pipeline discipline onto Databricks' lakehouse and GCP, the platforms enterprises are standardising around for large-scale analytics and AI workloads. The validation layer travels with the pipeline. It is not rebuilt from scratch for each logo.
Already live
Clay has already built and delivered an AI-assisted validation layer on top of a real data integration services pipeline, for a banking client.
A Talend-based pipeline ingests and validates live transaction data including PAN, amount, timestamp, and country, and applies a set of fraud-detection rules at the same time: annual limit checks, large-transaction flags, rapid-transaction sequences, multi-geography activity, and structuring patterns designed to stay under a threshold. Anything that trips a rule gets risk-classified and routed into a centralised alert table, instead of relying on a reviewer to notice it by eye.
Flag the record the moment it happens. Don't wait for next quarter's audit.
That is the same principle every data migration services engagement needs, aimed at a different problem: rule-based, automated flagging of the record that doesn't look right, running continuously, not a manual QA pass at the end of a project. Applied to a general database migration, the field that mapped wrong, the record that was duplicated, or the value that is structurally valid but contextually implausible gets flagged when it happens.
The shape of the job
Treating every migration as the same shape is where timelines slip. Clay scopes which model fits before writing a single mapping rule. The model determines the plan.
What is needed is a hard cutover, done in a defined window, with the old system switched off the moment the new one is validated. Fastest to execute, least forgiving of an undetected data issue, which is exactly why the AI-flagged validation layer matters most here.
The way forward is a phased migration, module by module or business unit by business unit, so risk stays inside one slice of the operation. AI validation runs on each slice before the next one starts, so a bad map does not get copied into the rest of the estate.
Adoption happens through a parallel run, with synchronization keeping both systems in agreement until confidence is high enough to cut over. It costs more in infrastructure during the overlap. It costs significantly less in risk. AI reconciliation is what makes the overlap evidence, not a second source of doubt.
At the scale that stresses a pipeline
Banking
A banking client's transaction data — PAN, amount, timestamp, geography — now runs through a Talend pipeline that applies five fraud-detection rules at once and routes anything suspicious into a centralised alert table, cutting dependency on manual monitoring. The same architecture, aimed at a migration, flags the field that mapped wrong, the record that duplicated, or the value that is structurally valid and contextually implausible.
See the banking work →MOST
MOST needed a capital-markets application that could hold up under real trading conditions, on desktop and seamlessly across iOS and Android. Clay's testing and development work caught issues early enough to save real time and cost, and the reliability gains helped MOST strengthen its position in a competitive securities market.
Read the case study →iCix
iCix needed a document-management system secure enough for live operations, including Coca-Cola, Walmart, Costco, Hasbro, Hamleys, and Raley's, without a disruption to those clients. Clay ran sprint-based testing through Jira, validated data integrity in production, and synchronised automation scripts against every new release, so security and continuity held while the system kept shipping.
Read the case study →Before you sign off
Tell us what you're moving, from where, to where, and what "can't afford to lose" means for your data. We'll scope the pipeline, the validation rules, and the migration model that fits, before anything moves.
