AI features inside existing products
Add summarization, drafting, search, decision support, or workflow acceleration to the product your users already depend on.
AI integration services
Production AI systems for teams that need more than a prototype
Corvus Tech helps product and engineering teams ship AI integrations, agent workflows, and retrieval-backed experiences that work in production. We combine model integration with the surrounding application, API, and operational work required to make the system useful after launch.
OpenAI and Claude integrationsAgent workflows and tool callingRAG systems and knowledge retrievalEvals, observability, and guardrails
Where teams usually bring us in
The strongest engagements start with one valuable workflow, one system boundary, and a clear operational owner.
Internal copilots and knowledge systems
Connect AI to documents, APIs, and operating data so teams can retrieve grounded answers instead of guessing from stale context.
Agentic operations workflows
Design multi-step flows that call tools, request approval, log work, and recover cleanly when real-world systems behave badly.
Where AI integrations usually fail
Most failures are delivery failures, not model failures. These are the patterns we design around early.
The model works, but the workflow does not
Teams often validate the prompt and ignore the surrounding operational steps. We scope the data access, tool permissions, fallback paths, and human review needed for the full job to succeed.
The pilot looks good and then quality drifts
Production AI needs evals, instrumentation, and regression checks. Without them, every prompt tweak or provider change becomes guesswork.
Nobody owns the application work around the model
Real delivery usually stalls in the API, frontend, auth, and deployment layers. We keep the AI work and product engineering in one team so the integration can actually ship.
What we build
Engagements usually combine model work with product and platform engineering so the release can reach users cleanly.
Delivery layers we own
- LLM integration, prompt design, and provider selection
- Tool calling, agent control flow, and approval checkpoints
- RAG pipelines, indexing strategy, and grounded response design
- Application UX, APIs, auth, and backend systems
- Production telemetry, evals, and operational playbooks
Buyer outcomes we optimize for
- Faster execution on repetitive, expensive workflows
- Better operator trust through reviewable outputs
- Lower delivery risk through narrow, shippable scopes
- Reduced rework by keeping AI and product engineering aligned
- Clearer paths from pilot to production rollout
How engagements typically work
We prefer a narrow first release with real production hooks over a broad proof of concept.
01
Scope the workflow and risk
We start with the use case, user path, data boundaries, and business rule exceptions. That gives us a narrow first release instead of an oversized AI initiative.
02
Ship the pilot with production hooks
The first release includes the monitoring, review, and logging needed to learn from real usage. We avoid throwaway prototypes that need to be rebuilt later.
03
Harden for rollout
We expand eval coverage, cost controls, fallback behavior, and operational visibility so the system can handle more traffic and more internal trust.
Platforms and integration surfaces
We work best when the AI system needs to interact with the software your team already runs.
Web apps and internal tools
Customer support and operations systems
CRM, ticketing, and workflow platforms
Knowledge bases, docs, and file repositories
Custom APIs, MCP servers, and internal services
Related proof and technical writing
These links show the mix of delivery work and technical depth behind the service.
Titles: AI-powered creative workflows
Node-based AI workflow editor with multi-provider generation and complex backend orchestration.
View the Titles project →Superwhisper: shipping AI for real users
Conversion-focused product delivery for an AI voice tooling company with a strong commercial surface.
View the Superwhisper project →Claude agent loop production guide
Our view on how to structure agent loops, budgets, and human review for production systems.
Read the guide →GPT computer-use API guide
Primary-source grounded guidance on browser automation and computer-use workflows.
Read the guide →If the workflow matters, design the AI system like a production system
We can help you define the first release, choose the right model and tooling approach, and map the operational work required to make the feature safe to roll out.
Discuss the roadmap