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Industry — 17

Technology & SaaS

Engineering capacity that unblocks the roadmap.

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Technology companies usually call us for one of two reasons: an AI feature stuck on the roadmap, or a platform starting to break at scale. In both cases we work inside your team, and when we hand over, your team runs it.

In short

How do you add an AI feature to an existing product?

What delays an AI feature is almost never the model: the missing pieces are an evaluation harness, a unit cost model and multi-tenant data isolation. Neuros builds those three, then develops the feature inside your repository and your release process. The tenant boundary is enforced at database level rather than by a filter in the application layer, and the test suite includes scenarios where one tenant asks for another's data.

What's hard in this sector

Engineering capacity that unblocks the roadmap.

01

AI feature debt

The AI feature on the roadmap keeps slipping because there is no evaluation set and no cost model.

02

Breaking at scale

The architecture that was right at launch returns as cost and latency at ten times the users.

03

Capacity

Hiring cannot keep pace with the product calendar; capacity is needed without lowering the bar.

What we can build for you

  • 01Adding an AI feature to your product, end to end
  • 02Platform scaling and cost optimisation
  • 03Multi-tenant architecture design
  • 04A dedicated product engineering team
  • 05Technical audit and a modernisation plan
  • 06Standing up an evaluation harness and AI observability

Our work in this sector

Sources

  1. 01OWASP Top 10 for Large Language Model ApplicationsOWASP Foundation · 2025
  2. 02DORA — DevOps Research and Assessment metrikleriGoogle Cloud · 2024

Frequently asked

Questions we get asked

Get in touch

Inside. Neuros engineers work in your repositories, your release process and your rituals; there is no separate delivery package. The reason is handover quality rather than speed: a team working separately leaves your engineers a codebase, while a team working alongside them leaves the habit of running it.

Leaving isolation to a filter in the application layer means depending on a condition that will eventually be forgotten. Neuros enforces the tenant boundary at database level — row-level security or a schema per tenant — and runs every query inside a tenant context. The test suite includes scenarios where one tenant requests another's data; if those tests break, the release does not ship.

Four variables are computed: average context length per request, output length, cache hit rate and retry rate. Neuros measures all four in production and converts them into a monthly cost per user. Surprise invoices usually come not from the model's price but from long context needlessly carried on every request; caching and context pruning alone bring the cost down materially.

Other industries

Let's assess your sector specifically

This page is the general frame. Let's find the actual bottleneck in your operation together, in a 30-minute call.

Book a callinfo@neuros.com.tr