Tray.io Automation

API Integration Services

Your systems already expose everything you need through their APIs. The reason the data still moves by hand is that nobody owns the layer in between. That layer is what API integration services buy you: your CRM, ERP, data warehouse and SaaS tools exchanging records automatically and verifiably, without an engineer on call every time an endpoint changes. Facet builds that layer and then runs it — and we can quote it the way we do because our own company works this way first. Facet's engineering, content, research and quality review all move through Agent Loops under human command, which is how a boutique team carries the throughput it does, and why the integration monitoring we sell is monitoring we already run on ourselves.

The integration discipline comes from operating our own loops

Most integration work fails after the build, not during it. A connector gets wired, it ships, and nobody monitors it. Three months later a field rename silently breaks the sync, records stop flowing, and the team discovers the gap by accident — during a customer escalation, a board prep, or an audit.

The dangerous API failure is not the one that errors. It is the one that returns 200 OK and writes nothing. That is Facet's foundational point of view on integration work, and it is not a position we arrived at from the outside. Facet's own delivery depends on data moving correctly between systems with nobody watching, and every failure mode below is one we hit on our own operations before we designed against it on a client's.

We know what a silent failure looks like from the inside. A job that keeps reporting success while writing nothing. A retry that quietly drops the record it could not place. A schema change that degrades output rather than breaking it, so the alerting never fires. Designing for those is not thoroughness; it is scar tissue.
It changes the commercial terms. The expensive half of an integration engagement has always been the reading — senior engineers going through your systems by hand to find out what connects to what. Agents take that inventory pass, which is why the audit is time-boxed and fixed-fee rather than an open-ended discovery phase.
Humans stay in command. Agents carry the watching and the volume; a named Facet engineer owns the architecture, triages every alert and makes every call about what gets promoted to production.

Why hire an API integration partner?

Connecting two systems is a week of work. Keeping forty endpoints correct through four years of upstream API changes is a different job, and it is the one that quietly consumes internal engineering teams who took on the first job and inherited the second.

Integrations fail at the edges, not the core. The connection between two systems looks stable on day one. What breaks it is the edge case nobody specced — a null field, a rate limit at peak load, a pagination change, a schema revision upstream that nobody flagged. A partner who has operated these before designs for those failure modes from the start rather than discovering them in production.
API integration is mostly maintenance, and maintenance is what gets cut. Endpoints deprecate, auth schemes rotate, payloads gain fields. Internal teams build integrations during a project and maintain them during whatever time is left over, which in practice is none.
Downtime nobody detects compounds quietly. Every hour of undetected integration downtime is an hour of decisions made on stale data, leads not routed, invoices not triggered, reports that do not reconcile. The cost is real and it never appears as a line item.
The platform decision is easier to get wrong than to get right. Over-buying an enterprise integration platform for six simple flows is expensive. Under-buying a lightweight tool for high-volume, heavily transformed data fails later and louder. That decision belongs to someone who has lived on both sides of it.

Show me the math

(Illustrative — the shape of the arithmetic matters more than our numbers, so re-run it on yours.) A mid-market operations team running three manual integration touch-points — data extracted from one system, transformed, and pushed to another by a person — might spend 15–20 hours a week across the team on those handoffs (illustrative). A well-designed, monitored integration covering the same flows can absorb most of that coordination time (illustrative), returning several hundred hours a year to higher-value work.

Your actual figure depends on your systems, your data quality and your current process maturity. The audit quantifies the real gap rather than assuming this one.

What the integration loop actually does

Facet builds integrations as Agent Loops rather than trigger-action chains that run until they quietly stop. The loop absorbs the high-volume, clearly checkable work — the passes that need doing every day and almost always find nothing.

Inventory every data flow before anyone builds anything. What syncs, what does not, what a person is moving by hand today, and what is partly automated but unmonitored. This is usually the first time anyone has seen the whole integration surface at once.
Read the API surface you actually have. Endpoints, auth schemes, rate limits, pagination behavior, webhook reliability and the gap between what each vendor's documentation promises and what its API returns. Agents are good at this because it is reading at volume, and it is the pass internal teams skip.
Profile the source data and surface the quality problems first. Inconsistent field values, duplicates, undocumented schema variations. Integration fails when source data is inconsistent, so this pass runs before the build rather than during it.
Move the records, idempotently. Duplicate triggers, API retries and race conditions are handled by design, so a re-run does not create a second copy of anything or corrupt what is downstream.
Watch every run, continuously. Execution logs, run frequency, record volumes, error rates and latency against a baseline. An unusual drop in record volume is often the earliest signal of an upstream problem.
Validate against the source on a sampling basis. Records that arrived are checked field-by-field against where they came from, so "the integration ran" and "the integration was correct" stay separate questions.

What a person still decides

Everything above is throughput. The judgment calls are named, and they stay with people.

What the data means. When your CRM and your ERP disagree about what an account is, the loop surfaces the conflict. Which definition is authoritative is a business decision with revenue attached to it.
Which platform the work belongs on. The audit produces the evidence; an engineer makes the call, and argues it with you rather than presenting it.
Every alert triage. When an integration errors, the alert goes to a Facet engineer, not to a channel your team is assumed to be reading. A person decides whether it is a transient failure, a schema change or the first sign of something larger, and writes down what was found and what was done.
Every promotion to production. Integrations move from development to staging to production with confirmed record counts and field-level accuracy checks at each step. Nothing is promoted because a loop said it was ready.

Choosing the platform is part of the work, not a precondition

Most teams arrive at API integration without having settled on a platform, and that is the right order. The platform is an output of the audit, not an input to it. We look at the systems, the volumes, the transformation logic and the governance requirement, and then we tell you which layer fits — including when the answer is cheaper than you expected.

Enterprise integration platforms, where the complexity earns them. Tray.io is where our bench is deepest: multi-step transformation logic, conditional branching, looping over record sets, custom scripting, audit logs, role-based access and SOC 2 alignment. It is the right answer when data volumes, transformation requirements and governance standards exceed what no-code tools are designed to handle — and the wrong answer when they do not.
Lightweight automation platforms, where the job is genuinely simple. For low-volume flows between mainstream SaaS tools, Make.com or Zapier are often the better choice: lower overhead, faster build, adequate for the job. We say so when that is what we find, and it costs us the larger engagement.
Direct against the API, when no platform is worth the tax. Some integrations are a scheduled job and a few hundred lines in your own repository. Paying a platform license to hold them is a recurring cost with nothing recurring behind it.
Whatever you have already licensed. If the platform decision is made and paid for, that is a constraint we design inside rather than an argument we re-open.

What an engagement delivers

Integration audit and data flow mapping

Every engagement begins with a structured audit before a line of integration logic is written: the gap inventory, a backlog ranked by expected yield (volume of records, frequency, downstream impact if it fails), a data quality and schema audit, and the platform recommendation with the reasoning attached.

Integration architecture and Agent Loop design

Each integration is designed with explicit trigger sources, data mapping, transformation rules, filter and routing branches and destination targets — documented so your team can read what is running and why. Error branches, failure conditions and retry logic are part of the design, not an afterthought, so failures surface as alerts rather than as missing records discovered weeks later. Monitoring signals are captured in the design phase, which is what makes integration health visible without opening a console.

Build, test and promotion

Staged promotion with validation at each step. Authentication and credential management — OAuth flows, scoped service accounts, token rotation — handled so your team does not carry that maintenance. And for any integration replacing a manual process, a parallel run: automated and manual outputs reconciled for a defined period before cutover.

How it runs after go-live

Monitoring and alerting

Execution logs are reviewed on a defined schedule rather than when something breaks. Failures alert a Facet engineer who triages, finds root cause and resolves, with a post-incident note recording what changed. Expected record volume and processing time baselines are set per integration, and deviation triggers a review.

The KPIs we track

The metrics that matter for API integration are not website analytics.

Integration uptime: the share of scheduled runs completing successfully. Your reliability baseline.
Record accuracy rate: the share of records arriving with correct field values, validated against source data on a sampling basis.
Mean time to detect and mean time to resolve: how quickly a failure is identified and fixed. We target same-day on both for high-priority integrations.
Latency per integration: time from trigger event to destination write. A CRM record that takes four hours to reach your support tool is functionally still a manual process.
Data volume trend: record counts over time. Unexpected drops and spikes are signals, not noise.

Alerting is configured as part of the build, never as an add-on afterwards, and clients running several integrations get a monthly integration health report — aggregate uptime, error trends and tuning recommendations in one view.

Iteration and tuning

APIs change, processes evolve, new systems join the stack. Each quarter we review the active integration inventory, flag what needs schema or logic changes, and propose the next candidate from the audit backlog. When an upstream vendor deprecates an endpoint or revises its data model, we absorb that change rather than handing it to your engineering team.

How could AI agents run your integration layer? Book the 45-minute Data Flow Audit on your real stack. You leave with your data flows inventoried and ranked by what a broken one would cost, an illustrative estimate of the hours going into manual handoffs today, and a written recommendation — including "these flows do not need an integration platform at all" when that is what we find.

Ask your integrations the second question

Most teams can answer the first one. Is it running? The dashboard says yes, the run history is green, nobody has complained. The second question has no ready answer anywhere in the stack: is it correct? Are the records that arrived the records that left, field for field, in the volumes you would expect — or has something been returning 200 OK and writing nothing since a schema change in March?

That question is answerable on one flow, this month, without building anything. Take the integration whose failure would cost you the most, pull a hundred records from each side, and check them field-by-field against the source. It is about an hour and it produces a number: the share of rows that are right. Most teams have never had that number for any integration they own, which is why "the sync is fine" is a belief rather than a finding.

It is also the evidence that settles this, because it is arithmetic on your own rows. Nobody has to take a claim about it on trust. A person in your company who knows what the number should be can read the reconciliation and tell you in a minute whether it holds — and if it does hold, you have learned your integration layer is in better shape than you feared, which is a result worth a day.

Then the payoff compounds in a direction most teams do not price at the start. The day you change CRM, or billing, or the warehouse underneath everything, a company whose systems are joined by a maintained and verified layer swaps an endpoint and re-points a mapping. A company whose systems are joined by point-to-point scripts written by whoever was free that quarter rebuilds the lot, and usually discovers halfway through that nobody documented what the old ones were doing. We have been called into both versions. A verified integration layer is not what makes your current stack work — it is what makes your next stack a decision rather than a project, and that is a position you take deliberately rather than arrive at.

Where the integration layer goes next

A reliable data layer is the rung the rest of the ladder is built on, and stopping here is a complete outcome.

Agentic Engineering — the build layer, when the work the data triggers needs real software rather than configuration.
AI Managed Services — the run layer. A named Facet team keeps the integrations and everything downstream of them earning as your systems move.
Agentic Operating System — the same pattern across every operating area, so the data does not just move but triggers the work it should.
Forward Deployed Engineering — a senior Facet engineer embeds and transfers the method if you would rather own this capability in-house.

If your integrations are lighter than an enterprise platform warrants, our workflow automation and Make.com practices cover the same discipline at a smaller scale — ask and we will point you at the right one.

Become the business that runs on reliable data

Every manual integration handoff your team runs today is a constraint on how fast you can move: on reporting latency, on lead response time, on billing cycle speed, on the quality of the decisions your leaders make from the data in front of them.

Facet Interactive has been building and running integration engagements since 2014, and was named a CIOReview Top 10 Digital Transformation Services Company in 2021. Our team of 40 across 9 timezones runs integrations around the clock. We do not hand you a connector and walk away — we are the named team accountable for keeping it working.

How could AI agents run your integration layer? Book the 45-minute Data Flow Audit — no slide deck. You leave with a prioritized view of your current integration gaps, an illustrative estimate of the hours your team spends on manual data handoffs, and a written recommendation you keep whether or not you continue with Facet. Sometimes that recommendation is that no integration platform is worth its license for the flows you have, or that the honest first step is fixing data quality in a source system rather than building anything at all. When that is the finding, that is what the document says.

FAQ

What do API integration services actually include?

Everything between "these two systems should share data" and "they still do, three years later." A build alone is the cheap half; the durable half is the audit that precedes it and the monitoring that follows it.

Discovery and audit: every flow inventoried, the API surface of each system read end to end, source data profiled for the quality problems that derail integration work.
Platform recommendation: which layer the work belongs on, with the reasoning written down rather than asserted.
Build and staged promotion: development to staging to production with field-level accuracy checks at each step, and a parallel run against the manual process before cutover.
Authentication and credential management: OAuth flows, scoped service accounts and token rotation handled, so an expired credential is not how you find out an integration existed.
Monitoring, alerting and schema-change management: the ongoing half. Failures route to a named engineer, and upstream API changes are absorbed rather than escalated to your team.

Which integration platform should we use — Tray.io, Make.com or Zapier?

It depends on volume, transformation complexity and governance, and the audit answers it with evidence rather than preference. Facet works across the landscape and matches the platform to the problem.

Enterprise integration platforms fit API-heavy, multi-system environments: connections to virtually any system with a REST or GraphQL API including internal and custom-built applications, multi-step transformation logic, conditional branching, looping over record sets and custom scripting. Tray.io is where our bench is deepest, and it earns its cost when this is genuinely the shape of the problem.
Governance can decide it on its own: audit logs, role-based access, SOC 2 alignment and the ability to restrict which data flows where. Most no-code tools are not designed for that, and in a regulated environment that is the whole argument.
Throughput can decide it too: high-volume, high-frequency record flows that would hit rate limits or performance ceilings on a lighter platform.
Simpler tools fit more often than vendors admit: for low-volume flows between mainstream SaaS tools, Make.com or Zapier are often the better choice.
Sometimes the answer is no platform: a scheduled job in your own repository, with monitoring around it, costs less to own than a license.

How long does an API integration take to go live?

A well-scoped single integration typically moves from audit to production in four to eight weeks, driven by complexity, source data quality and the number of flows in scope.

Audit and scoping (weeks 1–2): gaps inventoried, candidates ranked, the first integration scoped end to end.
Build and staging (weeks 2–5): built and validated in staging with field-level accuracy checks against your actual data.
Parallel run and promotion (weeks 5–8): automated and manual outputs reconciled before production cutover.
Data quality is the most common schedule risk: inconsistent field values, duplicates and undocumented schema variations extend the cycle, which is why the audit surfaces them before development begins.

How does Facet price an API integration engagement?

Project-plus-monthly, with both numbers quoted before work starts and no hourly billing. The audit and build are scoped and priced as a fixed-fee project; ongoing monitoring, error resolution, schema-change management and quarterly reviews are a monthly arrangement priced on the number and complexity of active integrations.

The audit produces the number: we do not quote integration work from a feature list, because the estimate comes from your actual systems and data flows.
Platform licensing is separate: where an integration platform is warranted, the vendor bills its license directly. We give you a clear picture of the total — services plus licensing — before any commitment.

Can Facet manage integrations our team already built?

Yes, whether we built them or not — after an assessment. We review the integration architecture, error-handling design, monitoring configuration and historical execution logs to establish what we would be inheriting.

Common findings: missing error branches that silently drop records, no monitoring or alerting, credentials that have expired or will, and transformation logic nobody currently on the team can explain. All fixable, but they need surfacing and pricing before we take operational ownership.
No lock-in on the assessment: it is a standalone engagement. If you decide afterwards to keep operations in-house using what we surfaced, that is a valid outcome and we would rather you have a clear picture than a contract.