Tenant-safe by design
Multi-tenant ingestion with per-customer isolation, quotas and fair scheduling.
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For ad-tech & SaaS platforms
The integration team behind ad-tech and SaaS products.
Amazon Ads, SP-API, Walmart, Google, Meta and TikTok integrations built into your product and kept working as the APIs change, by engineers who work in your codebase and your sprints.
- 30+seller accounts on the SP-API platform we built for Protego
- 5 minfrom ingestion to risk score for Protego
- 2Pixis products we engineered with their team
What you get
Integrations that keep working while your team builds the product.
Tokens that stay alive
OAuth and credential lifecycle handled across customer accounts, including expiry recovery.
Rate limits respected
One large customer can’t degrade service for the rest.
Built in your codebase
Work lands as pull requests in your repo, through your code review and CI.
Sound familiar?
Integrations are the roadmap tax.
- For a platform business, every ad network integration is a permanent commitment.
- It has to authenticate thousands of customer accounts, respect per-customer rate limits, survive API deprecations without downtime, and fail in ways your support team can diagnose.
- Each new network is quoted as a sprint and becomes a maintenance obligation forever.
- Before long, a meaningful share of engineering capacity is spent keeping integrations alive instead of building the product that differentiates you.
- Amazon meters aggressively and unevenly.
Before and after
What changes.
Integrations as a roadmap tax
- Every new network quoted as a sprint, then maintained forever
- One large customer can slow everyone else down
- A support ticket per API change
- Engineering time spent keeping integrations alive
With an integration pod
- New integrations built to your existing patterns
- Rate limit orchestration with per-tenant quotas
- Credential lifecycle handled, including expiry recovery
- Ingestion observability your support team can read
What we do
What we build for you.
Multi-tenant ingestion architecture with per-customer isolation, quotas, and fair scheduling
New platform and network integrations built to your existing patterns rather than beside them
OAuth and credential lifecycle handling across thousands of customer accounts, including expiry recovery
Rate limit orchestration so one large customer cannot degrade service for the rest
Ingestion observability your support team can actually read when a customer asks why data is stale
Migration work for API deprecations, scheduled ahead of enforced cutoffs
How we work with product teams
Inside your codebase, not beside it.
Audit the integration layer
Review the existing integration layer and the operational load it generates today
Build the first integration
Build one new integration end to end, establishing the pattern and the tests
Take over upkeep
Take over the maintenance burden so your team returns to product work
Add observability
Add observability and self-service diagnostics to reduce support volume
Engagement
An embedded pod, sized to your roadmap.
Senior engineers
Engineers who have built Amazon Ads API, SP-API, Walmart and Meta integrations before, not generalists learning on your product.
Your sprints and standups
Work lands as pull requests in your repo, through your code review and CI.
3-month minimum
Then month to month with notice on capacity changes. Anything shorter is mostly ramp-up.
Your IP
Code, docs and runbooks are yours. NDA before discovery.
Results
What we built, and what changed.
Risk scores in 5 min
Protego
We were engaged to architect and build the complete backend data infrastructure behind Protego: SP-API integration, report ingestion, risk-signal normalization and the scoring pipeline.
Read the case study
AI ad platform + creative studio
Pixis
TechEsthete worked as an engineering partner to Pixis on two parts of their product: the AI ad platform that manages campaign performance across channels, and the creative studio where teams generate and test ad creative.
Read the case study
One pipeline, every client account
SellerFuse
SellerFuse is an Amazon-focused platform for agencies. We built the pipelines, dashboards and automation engine behind it: Ads API and SP-API data unified in one system, with bid, budget and dayparting automation on top.
Read the case studyWhat clients say
In their words.
“They turned a fragile ETL setup into agentic workflows that monitor and optimize spend inside the rules we set.”
“They plugged in like an extension of our own team: production-ready systems with real engineering depth, not just dashboards.”
FAQ
What product teams ask us.
Would we be handing over core product engineering?
Not the differentiated part, and we would advise against that. The pattern that works is us owning the integration and ingestion layer, which is essential, specialized, and rarely where your product competes, while your team keeps the features customers choose you for.
How do you handle rate limits across thousands of tenants?
Centralized scheduling with per-tenant quotas and fair queuing, so heavy usage by one customer cannot starve others. Backfills run in separate windows from live syncs. Getting this wrong is the most common reason platform ingestion degrades unpredictably as customer count grows.
Can you work inside our existing codebase and standards?
Yes, and that is the default. We work in your repos, follow your conventions, and go through your review process. Integrations built beside a codebase rather than inside it become the next maintenance problem.
What about customer data security?
We work under your security model, with least-privilege access and no data leaving your infrastructure. Where you have compliance commitments to your own customers, we work within those controls and document what we touch.
How current can seller data realistically be?
It varies sharply by endpoint, and honest product decisions depend on knowing which. Some pricing and offer data can be near real time. Report-based data is asynchronous and typically lags by tens of minutes at best. Advertising performance restates for up to 14 days. Promising uniform real-time freshness across all of it is a promise the API cannot keep.
How do you avoid exhausting a seller's quota?
Quota is treated as a scarce shared resource with explicit priorities. Time-sensitive endpoints get reserved capacity, low-value polling is replaced by change detection, and consumption is tracked per tenant so you can see the ceiling approaching rather than discover it during a peak.
Have you built this before or would we be first?
We have built and operated Amazon automation at multi-tenant scale, including the SellerFuse work in our case studies. The failure modes here are specific and unforgiving, and prior exposure to them is most of the value.
Can you help us pass Amazon application review?
Yes. We build to the data protection policy requirements, including PII handling, retention, encryption, and access logging, and we have taken applications through review before. Retrofitting those controls after a rejection is considerably more expensive than designing for them.
Who owns the code we pay for?
You do. We work in your repo, under your code review, and everything we write is yours.
Will you sign an NDA before we share details?
Yes. We sign your NDA before discovery and a DPA before any customer data access.
How much overlap with New York hours?
Our Surrey, BC team is on Pacific time, three hours behind New York, so live overlap runs from late morning to the end of your day. Pod engineers join your standup at a time agreed at kickoff.
Tell us which integration is eating your roadmap.
Share the APIs and tenant count. An engineer will tell you how we'd approach it.