Technology
Generative AI Development Services — From Strategy to Production
End-to-end generative AI engineering — strategy, prototype, evaluation, and production for text, image, audio, and code.
What we build with Generative AI
- GenAI strategy: where it actually moves your business metrics
- Multi-modal builds: text, image, audio, video, and code
- Evaluation harnesses with golden datasets and automated regressions
- Guardrails, prompt-injection defense, and PII-safe data flows
- Cost modeling, model routing, and provider abstraction
- Hand-off with runbooks or ongoing operate-and-evolve
Why DiveScale
Built by engineers who ship Generative AI in production
Most generative AI projects stall between demo and production. DiveScale specializes in the engineering work that closes that gap — evals, guardrails, observability, cost discipline, and the model-routing layer that keeps the system maintainable as the foundation-model landscape shifts.
We start by asking what business metric the AI feature should move, not which model to use. Once the use case is qualified, we pick the right stack — frontier APIs, open weights, or a hybrid — and ship with measurable quality.
Operationally we cover the boring-but-critical surface: prompt versioning, drift monitoring, fallback models, rate-limit handling, and an audit trail your security and compliance teams can actually use.
Generative AI use cases we deliver
How we deliver
Our Generative AI delivery process
- 01
Strategy & qualification
A discovery sprint to qualify the use case, define ROI metrics, and decide what 'good' means in numbers — not vibes.
- 02
Prototype with evals
Working prototype plus an evaluation harness so quality improvements are measurable from day one.
- 03
Production hardening
Guardrails, observability, cost controls, prompt versioning, fallbacks, and security review.
- 04
Ship & evolve
We deploy, monitor, iterate on prompts, and roll forward as new models ship — or hand off with runbooks.
Related technologies
LLMs
Production LLM engineering — model selection, RAG, fine-tuning, evals, guardrails, and the operational layer that keeps quality high.
Learn moreAgentic Workflows
Multi-step AI agents that plan, call tools, write to systems, and stay inside policy — with human-in-the-loop checkpoints where it matters.
Learn moreOpenAI
Production-grade integrations with GPT-4o, GPT-4.1, o-series reasoning models, Realtime voice, embeddings, and the Assistants API.
Learn moreAnthropic (Claude)
Production builds on Claude Opus, Sonnet, and Haiku — long-context reasoning, tool use, prompt caching, and Computer Use agents.
Learn moreGenerative AI — Frequently Asked Questions
Discovery is typically a 2-week scoped sprint. A working prototype runs 4–6 weeks. Production builds depend on integrations and traffic. We share a fixed-fee proposal after discovery so the budget is known before commitment.
