Technology
LLM Development Services — Large Language Model Engineering
Production LLM engineering — model selection, RAG, fine-tuning, evals, guardrails, and the operational layer that keeps quality high.
What we build with LLMs
- Model selection across OpenAI, Anthropic, Gemini, and open weights
- Retrieval-augmented generation: chunking, embedding, re-ranking
- Fine-tuning, LoRA, and distillation when prompt engineering hits its ceiling
- Evaluation harnesses with golden datasets and CI-gated regressions
- Guardrails, prompt-injection defense, and PII-safe pipelines
- Observability with Langfuse, Helicone, or custom OpenTelemetry
Why DiveScale
Built by engineers who ship LLMs in production
LLM engineering is more than calling an API. The systems that survive contact with real users have evaluation, retrieval design, guardrails, and a model-routing strategy baked in. DiveScale has shipped these systems for healthcare, hospitality, fintech, and SaaS clients across the US and Europe.
We treat the model layer as swappable infrastructure. Application code targets an internal abstraction; the choice between Claude, GPT, Gemini, or open weights is a deployment decision — not a rewrite — so you can take advantage of new releases without bet-the-product migrations.
And we measure. Every system ships with a golden dataset and a regression suite, so quality changes are observable across model versions, prompt edits, and retrieval changes.
LLMs use cases we deliver
How we deliver
Our LLMs delivery process
- 01
Use case + eval design
We define success in numbers and build a golden dataset before writing a single prompt.
- 02
Architecture
Model abstraction, retrieval strategy, fine-tune-vs-prompt decision, cost model, and security posture.
- 03
Build + evaluate
Iterate on prompts, retrieval, and routing with quantitative quality signals on every change.
- 04
Operate
Drift monitoring, prompt versioning, model upgrades, and cost-per-query reporting — built in from day one.
Related technologies
OpenAI
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 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 moreMLOps
MLOps platform engineering — pipelines, model registries, evaluation, monitoring, and incident response for ML and LLM systems.
Learn moreLLMs — Frequently Asked Questions
Both, usually. Hosted APIs win at low-to-medium volume and on tasks where output quality is critical. Self-hosting wins on high-volume workloads, sensitive data, and offline use cases. We model the decision against your real traffic.
