01
LLM API integration
Assess LLM API integration in the context of structured output validation, including implementation choices, quality checks, operational constraints and ownership during real delivery.
/ Engineering service
Practical AI integration for products people actually use.
Connect models, retrieval and realtime interfaces to tested product workflows with clear evaluation, privacy and latency boundaries.
Technical scope
Applied AI & Product Integration — Technical scope
Every engagement is scoped against real technologies, existing systems and measurable responsibilities.
02
Structured output validation
Assess structured output validation in the context of retrieval and relevance evaluation, including implementation choices, quality checks, operational constraints and ownership during real delivery.
03
Retrieval and relevance evaluation
Assess retrieval and relevance evaluation in the context of realtime speech workflows, including implementation choices, quality checks, operational constraints and ownership during real delivery.
04
Realtime speech workflows
Assess realtime speech workflows in the context of prompt injection boundaries, including implementation choices, quality checks, operational constraints and ownership during real delivery.
05
Prompt injection boundaries
Assess prompt injection boundaries in the context of monitoring cost and latency, including implementation choices, quality checks, operational constraints and ownership during real delivery.
06
Monitoring cost and latency
Assess monitoring cost and latency in the context of LLM API integration, including implementation choices, quality checks, operational constraints and ownership during real delivery.
/ Engineering situations
Where this service helps
Engineering challenges we can help you tackle.
01 / 04
Add AI assistance to a real product workflow
Consider this situation: Add AI assistance to a real product workflow. Clarify the current system, dependencies and delivery boundaries, then agree on what success and operational validation would look like.
02 / 04
Evaluate an interview feedback pipeline
Consider this situation: Evaluate an interview feedback pipeline. Clarify the current system, dependencies and delivery boundaries, then agree on what success and operational validation would look like.
03 / 04
Introduce grounded enterprise knowledge search
Consider this situation: Introduce grounded enterprise knowledge search. Clarify the current system, dependencies and delivery boundaries, then agree on what success and operational validation would look like.
04 / 04
Stabilize a realtime voice feature
Consider this situation: Stabilize a realtime voice feature. Clarify the current system, dependencies and delivery boundaries, then agree on what success and operational validation would look like.
How we approach the work
Practical AI integration for products people actually use.
Connect models, retrieval and realtime interfaces to tested product workflows with clear evaluation, privacy and latency boundaries.
- 01 / 04
Discover systems and constraints
Map architecture, environments, business priorities, decision owners and current risks.
- 02 / 04
Agree technical ownership
Agree architecture choices, integrations, engineering responsibilities, test evidence and review cadence.
- 03 / 04
Build and validate
Deliver in reviewable increments with appropriate security, QA, performance and release checks.
- 04 / 04
Operate and improve
Use feedback from production, users and the engineering team to improve the system deliberately.
Questions about the engagement
Discuss the work before deciding the team.
01How is applied ai & product integration scoped?
Scope depends on the existing architecture, integrations, release constraints and the outcome you want. We first clarify boundaries, risks and technical responsibilities.
02Can you support LLM API integration with a small team?
Yes. A focused specialist or a cross-functional engineering group may make sense depending on the workload and ownership required. We confirm the model for each engagement.
03How do you evaluate engineers for structured output validation?
We look for evidence in real code, system decisions, failure analysis, tests, deployment practices and the responsibilities the engineer has personally owned.
04What information helps you assess our project?
Share the current system, intended changes, relevant stack, timeline and how your team works. We can then propose a practical discussion and next steps.
/ Engineering service
Practical AI integration for products people actually use.
Engineering support shaped around your systems, priorities and delivery needs.
/ Client recognition
Proof in the partnership.
Independent feedback from the teams we've worked with.
5.0
“Onectus technologies showcased their expertise, professionalism, and commitment to delivering a high-quality solution.”
CEO, Furnistudio.com
Furniture configurator & commerce
Verified on Clutch
4.5
“We liked the flexibility of their developers.”
CTO, FinTech Company
Fintech product engineering
Verified on Clutch
5.0
“Put simply, they were perfect.”
Head of Software Engineering, Social Shopping Group
Global shopping platforms
Verified on Clutch