Connector strategy
Batch, incremental, or event-based integration with explicit reliability decisions.
Data engineering
Data integration and connectors from SaaS, APIs, ads, CRM, and product databases
Managed and custom connectors for Stripe, Shopify, CRM, ads, billing, product databases, and operational exports into a controlled reporting and analytics layer.
Inspectable delivery artifact
A buyer should see the operating evidence, not only read the promise.
This service is evaluated through source contracts, quality checks, owners, failure states, and handover evidence. The preview shows the kind of artifact that belongs in the delivery packet.
Data integration and connectors from SaaS, APIs, ads, CRM, and product databases
Sample acceptance matrix
source_contract
Check
owner + grain
State
pass
Owner
Data eng
pipeline_run
Check
retry + replay
State
pass
Owner
Platform
dashboard_view
Check
certified mart
State
review
Owner
Business
| Artifact | Check | State | Owner |
|---|---|---|---|
| source_contract | owner + grain | pass | Data eng |
| pipeline_run | retry + replay | pass | Platform |
| dashboard_view | certified mart | review | Business |
Common sources
Billing, CRM, ads, commerce, product events, support, and export-based systems.
Reliability controls
Incremental sync, rate limits, retries, schema drift detection, and owner-visible failure states.
Where this fits
Use this service when a business process already depends on data, but the current path from source system to accepted metric is fragile, undocumented, or too dependent on one person.
DataDost AI provides integration design for SaaS platforms, APIs, ads, CRM, billing, support systems, product databases, and operational exports for growing companies.
What we build
▾
Source inventory, API feasibility, extraction method, sync frequency, schema strategy, retry model, and source owner registry are defined together.
Source inventoryConnector feasibility reviewAPI and authentication designIncremental sync logicSchema drift handlingRetry and failure modelLoad metadata tablesSource owner register
Engineering decisions we make explicit
▾
API polling cadence, pagination handling, rate limits, deleted-record reconciliation, replay strategy, and monitoring ownership are all decided in the design document.
Controls and quality checks
▾
Integration controls include schema validation, deduplication keys, load metadata, API error capture, and handling of partial loads with business impact visibility.
Each connector has a documented ownerSync schedule is visibleFailures are loggedDuplicate loads are preventedSchema changes are detectedPartial loads are handledCredentials are not embedded in codeDownstream dependencies are mapped
How we work with client data
▾
Integration work starts with minimum necessary access and clear owner approvals before deeper implementation.
Pilot-first onboarding using exports, snapshots, or read-only accessLeast-privilege roles and credential handling by ownerClient-owned infrastructure preferred for runtime and data layersSmallest sufficient field set for each integration scopeAccess log and expiry process for all credentials
What handover looks like
▾
Handover covers connector catalog, credential model, sync schedules, failure behavior, and dependency maps for adding new sources safely.
Architecture summary with assumptions and trade-offsSource inventory and ownership notesRunbook for routine operation and failure handlingKnown limitations, backlog, and scale-up recommendationsBusiness-facing explanation for stakeholders
How we demonstrate this service
▾
We prove integration behavior by wiring a representative source set and stressing realistic failure and drift scenarios instead of showing only happy-path mockups.
Connector setup for CRM, billing, ads, and product/events sourcesScheduled sync behavior and per-source latency visibilityControlled schema-drift and partial-load recoveryCredential lifecycle flow with rotate/revoke simulationPropagation checks from source changes to dependent outputs
What you can review in the demo
▾
The dashboard-like flow keeps impact and ownership explicit from source intake to business output.
Connector contract table (source, SLA, owner, auth, frequency)Load logs for normal and failed synchronizationSchema drift fix and remediation timelineDependency map from source systems to reportsRecovery evidence for partial-load correction
Why this matters▾
Most teams fail not from one source issue, but from source coordination failure across many systems.
Security and data handling
Access is role-based, production changes are documented with rollback notes, and handover includes access assumptions and evidence.
Working surface
Map the first reliable data path.
Bring the source exports, dashboards, and decisions that currently create friction. We return a scoped pilot path with owners, evidence, and handover expectations.