Generative AI That Works in Production
MetaSys builds production-grade generative AI for US enterprise: LLM applications, RAG pipelines, AI copilots, and fine-tuned models, all shipped with evaluation frameworks, grounding guardrails, and full observability.
GenAI demos are easy. Production GenAI is hard.
A language model responding coherently in a demo is not the same as a system that is accurate, grounded, affordable, and safe at production scale. Four problems kill most generative AI projects before they ship.
Demos hallucinate. Production cannot.
A chatbot that makes up answers works fine in a controlled demo. In production it erodes trust in minutes. Grounding, source citation, and confidence thresholds are not optional add-ons. They have to be designed in from the start.
Retrieval quality determines output quality.
Most RAG failures are retrieval failures, not model failures. If the chunking strategy, embedding model, or reranking logic is wrong, no amount of prompt engineering will fix the outputs. Getting retrieval right is half the work.
Evaluation is skipped until it is too late.
Teams ship without a systematic way to measure accuracy, coverage, or regression. When the model updates or the data drifts, there is no baseline to compare against and no alert to catch degradation.
Cost and latency are afterthoughts.
A system that costs $4 per query or takes 8 seconds to respond will not survive contact with real usage. Token budgeting, caching, streaming, and model tiering have to be part of the architecture, not a post-launch patch.
MetaSys structures every engagement to address these before they become expensive. See our data and AI platform capabilities.
Six GenAI system types we ship to production.
Every system is scoped to a specific business problem, evaluated on your real data, and deployed with observability and guardrails. These are the generative AI system categories we build most often.
LLM applications
Structured applications built on top of large language models: document analysis tools, classification engines, knowledge Q and A systems, and content pipelines. Designed for throughput, latency, and cost from day one.
Enterprise, SaaS, Legal, FinanceRAG systems
Retrieval-augmented generation pipelines that ground every response in your actual documents, databases, or knowledge bases. We handle chunking, embedding, indexing, reranking, and citation so outputs are traceable and accurate.
Healthcare, Legal, EnterpriseAI copilots
Embedded assistants that work inside your existing product or internal tool. Answers questions, drafts content, surfaces relevant records, and hands off to humans when confidence falls below threshold.
SaaS, Operations, SupportFine-tuned domain models
When a general-purpose model does not perform well enough on your specific vocabulary, format, or reasoning style, we fine-tune on your data. Smaller, faster, cheaper, and more accurate than prompting a frontier model.
Logistics, Healthcare, FintechEvaluation and evals frameworks
We build the measurement layer alongside the system: automated eval sets, LLM-as-judge pipelines, regression suites, and production dashboards. You know if accuracy drops before your users do.
All domainsGuardrails and safety layers
Output filtering, topic restriction, PII redaction, toxicity detection, and prompt injection defense. Built for regulated sectors where a single bad output has real consequences.
Healthcare, Fintech, EnterpriseNot sure which type fits your use case? Book a scoping call and we will map the right architecture to your problem.
The five phases behind every production GenAI system.
Every engagement follows this process. It is designed to resolve the retrieval, evaluation, and safety problems that kill most generative AI projects before they reach users.
Use-case scoping
We map the task the GenAI system will own: input data, required output format, accuracy targets, latency constraints, and the cost ceiling that makes the system viable at scale.
Data and retrieval audit
We assess your data: quality, structure, volume, and sensitivity. We design the retrieval strategy, chunking logic, and embedding approach before writing any application code.
Build with evals wired in
We build iteratively against real data in staging. An evaluation framework is in place from the first iteration, so every model or prompt change is measured against a baseline.
Guardrails and security review
We add output grounding, confidence gates, PII handling, and prompt injection mitigations before anything reaches production. Compliance review is part of this phase for regulated sectors.
Deploy and observe
We deploy with full observability: query traces, latency dashboards, cost per request, and accuracy monitors. Managed operations are available if you want the system tuned and improved over time.
MetaSys is headquartered in Missouri with delivery teams in the UK and Pakistan. Every US engagement runs in US time zones with a dedicated delivery lead available during your business hours. Our GenAI practices are SOC 2-aligned and HIPAA-ready. We build with CCPA and GDPR awareness for any system that handles personal data. Data handling agreements are part of every engagement scoped for regulated sectors.
What separates our GenAI work from the rest.
Evals before launch, not after
We wire an evaluation framework into the first build. Accuracy, groundedness, and latency are measured from day one, not investigated after a complaint.
Retrieval quality is our first priority
We treat the retrieval layer as the most important part of any RAG system. Poor retrieval cannot be fixed with a better prompt. We get it right at the architecture stage.
Model selection based on benchmarks
We benchmark candidate models on your actual data before committing to one. The right model is the one that performs best on your task at the cost and latency you can sustain.
You own the IP and the infrastructure
The code, pipelines, embeddings, and fine-tuned weights are yours. We do not use proprietary runtimes you cannot inspect or migrate away from.
"MetaSys did not just build what we described. They asked the right questions up front, spotted three edge cases we had missed, and shipped a system that actually runs in production. The accuracy held up on real data from day one."
Zika
GMetrics, Germany
Generative AI development: what clients ask before starting.
How much does generative AI development cost?
Scoped GenAI engagements typically start from $30,000 for a single production LLM application or RAG pipeline. Fine-tuned models, multi-system copilots, and managed operations are priced based on data volume, model complexity, and integration depth. We provide a fixed-fee proposal after a scoping call.
Which LLM models do you use?
We select the model for the task. For general-purpose applications we work with GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro. For domain-specific systems where latency or cost matters, we fine-tune Llama 3 or Mistral on your data. The model is chosen based on accuracy benchmarks, cost per token, and the sensitivity of your data.
How do you prevent hallucinations in production?
We combine retrieval-augmented generation with output grounding checks, citation enforcement, and confidence-threshold guardrails. Every response that reaches a user is traceable to a source document or a structured data record. For high-stakes domains we add a review gate before output is surfaced.
How is my data kept private?
We build on infrastructure you control or approve: private VPC deployments, Azure OpenAI Service, AWS Bedrock, or on-premise open-weight models where data cannot leave your environment. We do not send your proprietary data to third-party model APIs without your explicit sign-off on the data flow. Our practices are SOC 2-aligned and HIPAA-ready.
How long does it take to build a production GenAI system?
Most clients have a first working RAG system or copilot in staging within 2 weeks of starting the build phase. Production deployments with evaluation, guardrails, and integration testing typically take 6 to 10 weeks end to end. Fine-tuning a domain model adds 2 to 4 weeks depending on data readiness. We confirm timelines after scoping.
How do we get started?
Book a 30-minute scoping call with a GenAI Architect. Bring a use case or a workflow you want language AI to handle. Most clients hear back within one business day.
Have a question we have not answered? Ask our team directly.
Ready to ship your first production GenAI system?
Bring a use case or a workflow you want language AI to handle. Walk away from the first call with a scoped architecture and a clear path forward.
30-minute call, no commitment. Most clients hear back within one business day.