What We Do • Integrate
Integrations that just work.
We build platform extensions and connectors that add capability and keep systems in sync.
Connectors that keep systems in sync.
Extensions that survive upgrades.
Reporting you can defend.
How is Integrate the right fit?
Integrate is a good fit when work needs to span multiple systems and correctness is important - especially if the integration is what you are selling (or what keeps your customers) or if your team is manually reconciling data; shipping features that have dependencies on multi-system workflows or developing extensions to other platforms, we provide the ability to connect the entire stack and standardize shared data without creating a dependency on future support.
If AI will be part of the workflow you are building, we will validate its feasibility first and then make it both traceable and operational prior to being moved into production.
- Manual reconciliation is repetitive - you want to ensure that systems remain in sync and there are validation checks and checkpoints.
- Partial failures are causing you financial loss - missing records, drifted mapping and "successful" jobs with incorrect results are reducing margin, throughput, renewals or trust.
- You are building an extension into another platform and you need packaging, upgrade posture and support boundaries defined prior to when customers rely on the extension.
- The deal and on-boarding process relies on the integration - you want the workflow to feel native in the customers' stack so that the users do not experience delays during on-boarding.
- You want to incorporate AI in the workflow, however, you require guardrails, traceability and an operating plan so the output can be defended.
Why integrations break
Most integrations start as "just move the data" until they become an incident - or a commercial problem. In regulated workflows, the risky failures are partial failures: jobs that succeed on paper but produce incorrect results.
- The integration rules are implied: Fields diverge and no one realizes until the deadline (or a customer does).
- Failures are invisible: Operators are informed too late about problems from downstream effects.
- Ad-hoc recovery: The only way to resolve issues is through a developer sprint or a complete rerun.
- Upgrades disrupt behavior: Customizations were not designed to survive platform updates.
What does success look like?
Success is not "the job ran". Success is a workflow that your customers and operators can trust - one that increases adoption, ensures data consistency among systems and creates no ongoing support burden.
Adoption without friction
The integration feels native within the tools that people are already using - on-boarding is easier, training is less time consuming and usage does not slow down post-launch.
Shared data, shared truth
The same records and definitions exist across platforms - fewer mismatched numbers, fewer surprises and outputs that you can justify.
Support stays sane
When things fail, it's visible and actionable - fewer escalations, faster triage, and operators are not stuck waiting on engineers.
Ship and keep shipping
The integration survives platform updates and version changes - releases are no longer fire drills, and customers do not get stuck on "that one configuration."
Integrate deliverables
We provide the integration and the state required to operate and sell it - shared definitions, clear integration rules, validation, failure visibility and recovery.
Connectors
Data movement with explicit mappings, validation and mismatch checks so that shared data remains consistent across systems.
- A versioned schema/mapping document along with automated compatibility checks.
- Conflict resolution rules (which system wins when data conflicts).
- Validation checks and mismatch reports (what's out of sync and how to fix it).
- Change detection (alerts when mappings/definitions shift) and change logs.
Platform extensions
Extensions that feel native in the target platform and are designed for adoption, upgrades and supportability.
- Packaging and installation process.
- Notes on compatibility and a process for upgrades.
- Boundaries of ownership (what you will support versus what the platform supports).
- Known checkpoints and actions for operator intervention.
- Optionally: usage tracking to measure adoption and friction.
Reporting
Reporting built from sources of truth that you can defend - with shared metric definitions across tools.
- Definitions for each metric (what it means and where it comes from).
- A clear source trail (where each metric comes from) and update process.
- Exceptions highlighted (missing, late, partial).
Hardening existing integrations
Turn brittle glue into workflows with clear visibility and reliable recovery to reduce re-work and escalations.
- Clear states and checkpoints.
- Safe retries and mismatch correction.
- Validation checks and change detection (alerts when mappings/definitions shift).
- Runbook for operators and escalation paths.
Platforms we integrate across
Examples from recent work. If a platform has an API or supported integration points, we can usually work with it - we confirm fit, constraints, and support posture during Discovery.
AI that helps and stays trustworthy
We can create AI-enabled features/tools/automation. We'll begin with understanding the desired outcome and what can't go wrong - and if AI isn't the right fit, we'll say so. When AI is the right tool, we add guardrails and traceability to ensure that its outputs can be verified, measured, and relied upon.
- Fit check first: baseline approach vs AI, plus a clear go/no-go gate.
- Measurable checks: evaluation criteria and acceptance tests defined before release.
- Guardrails: human review paths, constraints, and fallbacks where risk requires it.
- Traceability: logged inputs/outputs, versioning, and user actions for auditability.
- Operations plan: monitoring notes, alert routing, and a runbook.
Ways to engage
Discovery
Turn uncertainty into a plan.
Includes:
- Clarify the real problem, who it's for, and what success means.
- Map workflows, data sources, constraints, and risks up front.
- Define acceptance criteria and a Definition of Done everyone agrees on.
Project
Deliver a run-ready capability.
Includes:
- Milestone delivery with evidence to validate progress.
- Designed with auditability, recoverability, and security in mind.
- Clean handoff with documentation, knowledge transfer, and operational readiness.