LLM Application Development

LLM Application Development - From Prototype to Production

We build production-ready LLM applications with proper guardrails, evaluation, cost optimization, and monitoring. Not just a wrapper around an API - engineered systems that handle real-world complexity, volume, and edge cases.

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Production GuardrailsCost OptimizationEvaluation Frameworks3 LLM Products Shipped

Production LLM application development that goes beyond demos

Any developer can call an LLM API. Production requires guardrails, evaluation frameworks, cost management, and monitoring for quality drift. We have shipped LLM applications across FlowFin, Veritas, and Janus - and help you choose the right model for each task based on quality, cost, and latency.

Production Engineering

Guardrails, error handling, fallback strategies, rate limiting, and monitoring. The engineering that makes LLM apps reliable at scale.

Cost Optimization

Model routing (expensive models for hard tasks, cheap models for simple ones), caching, batching, and prompt optimization to control inference costs.

Evaluation and Testing

Systematic evaluation frameworks with automated testing, human evaluation loops, and accuracy tracking over time.

Model-Agnostic

We work with Claude, GPT-5, Llama, Gemini, and domain-specific models. We help you choose the right model for each task, not lock you into one vendor.

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Production LLM application development capabilities

Specific, concrete deliverables - not vague promises. Here is what you get.

Model Selection and Evaluation

Compare models (Claude, GPT-5, Llama, Gemini) on your specific use case. Benchmark quality, cost, and latency. Choose based on data, not marketing.

Prompt Engineering and Optimization

Systematic prompt development with version control, A/B testing, evaluation datasets, and regression testing. Not ad-hoc prompt tweaking.

RAG Integration

Ground LLM outputs in your data for accuracy. Retrieval pipelines, citation generation, and confidence scoring.

Guardrails and Safety

Output validation, content filtering, PII detection, error handling, and configurable safety policies for your specific requirements.

Cost Optimization

Model routing across tiers, response caching, request batching, prompt compression, and smaller models for simpler tasks. Control your inference spend.

Evaluation Framework

Automated testing suites, human evaluation workflows, accuracy metrics, regression detection, and quality dashboards.

Production Deployment

Streaming responses, load balancing, rate limiting, monitoring, alerting, and graceful degradation when APIs are slow or down.

Continuous Improvement

Feedback collection from users, prompt iteration cycles, model upgrades, drift detection, and ongoing quality optimization.

The numbers that matter

3

Production LLM products shipped (FlowFin, Janus, Veritas)

30+

LLM-powered tools across our product portfolio

Multi-model

Claude, GPT-5, open-source models in production

Sub-second

Streaming response latency in production

Real deployments, real results

Finance Automation

FlowFin AI Assistant

30+ LLM-powered tools for finance operations with human-in-the-loop confirmation on write operations

Content and Marketing

Veritas content platform

LLM-powered content generation with knowledge grounding, citation, and SEO optimization

Staffing and Recruitment

Janus recruitment platform

LLM-powered resume parsing, candidate matching, and hiring recommendations at scale

The Optivus Method

Every engagement follows four phases. You always know what is being delivered and what comes next.

01

Scope

Define the use case, evaluate model options, establish quality benchmarks, and design the application architecture. Prototype with your actual data.

02

Build

Develop prompts with systematic evaluation, integrate RAG if needed, implement guardrails, and build the application layer. Weekly demos with real outputs.

03

Ship

Deploy with monitoring, cost tracking, and quality dashboards. Load test for production volume. Train your team on prompt management and monitoring.

04

Scale

Optimize costs based on real usage patterns, iterate on prompts based on user feedback, upgrade models as better options become available.

Learn more about The Optivus Method

Products we have built with this technology

We do not just consult - we build production AI products. The same engineering powers what we deliver for clients.

Industries we serve

Our AI expertise transfers across industries. The underlying technology applies regardless of domain.

Ready to discuss your project?

Book a 30-minute call. Tell us about your workflow and we will scope the right approach together.

Common questions about llm application development

It depends on your use case. Claude excels at long-form reasoning and code generation. GPT-5 is strong at general tasks and has broad tool support. Open-source models (Llama, Mistral) work for simpler tasks and keep data on-premise. We benchmark multiple models on YOUR data and recommend based on quality, cost, and latency requirements - not vendor preference.
Multiple strategies: RAG for grounding outputs in real data, structured output validation to catch factual errors, confidence scoring to flag uncertain responses, citation requirements so users can verify claims, and systematic evaluation to measure hallucination rates. The goal is not zero hallucinations (impossible) but acceptable accuracy for your use case.
We use enterprise API agreements that prevent your data from being used for model training. For highly sensitive data, we can deploy open-source models on your own infrastructure so data never leaves your environment. We implement PII detection and redaction where needed. Every deployment follows your compliance requirements.
It varies dramatically by model and usage. Claude Haiku costs roughly $0.25 per million input tokens; Claude Opus costs roughly $15. GPT-5 is around $2.50 per million input tokens. For most enterprise applications processing hundreds of requests per day, monthly inference costs run $100-2000. We optimize by routing simple tasks to cheaper models and using caching to reduce redundant calls.
A focused LLM application targeting one use case typically takes 3-6 weeks from kickoff to production. More complex applications with multiple LLM tasks, integrations, and evaluation frameworks take 8-16 weeks. We ship working demos every week from week one.
Yes. We work with Llama, Mistral, and other open-source models when they fit the use case. Open-source is best when data privacy requires on-premise deployment, when cost sensitivity is high and the task is relatively simple, or when you need fine-tuning for a specific domain. We help you evaluate whether open-source or commercial APIs are the right choice.
Prompting is the starting point - it is fast, cheap, and works for most tasks. RAG is for when the model needs access to your specific data. Fine-tuning is for when you need the model to learn a specific behavior pattern or domain language. Most applications use prompting + RAG. A small percentage need fine-tuning. We recommend the simplest approach that meets your quality requirements.
We build fallback strategies: primary and secondary model providers, graceful degradation (show cached results or simpler outputs when the LLM is unavailable), retry logic with exponential backoff, and monitoring with alerting. Production LLM applications need the same reliability engineering as any other critical system.

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