LLM Development Services
LLMs that are accurate, grounded, and production-ready.
Getting an LLM to demo is easy. Getting one reliable in production is the hard part — RAG over your data, fine-tuning where it pays, rigorous evals, guardrails, and cost controls. That’s what we build.
RAG
Grounded answers
Evals
Measured quality
Open+
Frontier & OSS models
84%
fewer hallucinations with proper RAG
#1
risk: confident but wrong answers
0
evals on most “shipped” LLM features
60%+
cost cut with the right architecture
Production LLMs
The demo is easy. Production is hard.
Any team can get an LLM to look impressive in a demo. Making it accurate, grounded, measurable, and affordable when it’s answering real users — that’s the work. Off-the-shelf models don’t know your domain or your data, answers are confident but sometimes wrong, and without evals you have no idea whether quality is improving or quietly regressing.
We build LLM systems the right way: retrieval-augmented generation over your knowledge, fine-tuning and alignment where it genuinely helps, evaluation suites that catch regressions before they ship, and guardrails plus cost controls that keep the system safe and economical at scale.
Capabilities
LLM systems built for production
RAG pipelines
Retrieval over your knowledge base so answers are grounded in real, current data.
Fine-tuning & alignment
Tune models for tone, format, and narrow tasks where it measurably improves results.
Structured outputs
Prompt engineering and schemas that make output reliable and machine-usable.
Evaluation suites
Golden datasets and automated scoring so quality is measured and regressions caught.
Guardrails & safety
Filtering, validation, and constraints that keep responses safe and on-policy.
Cost & latency controls
Caching, routing, and right-sized models to keep the system fast and affordable.
Grounded vs Off-the-shelf
Why a raw LLM isn’t enough
A frontier model is powerful — but on its own it doesn’t know your data, isn’t measured, and gets expensive fast.
| Criterion | Production LLM (Ethersofts) | Raw LLM API |
|---|---|---|
| Knows your domain | RAG + fine-tune | Generic only |
| Accuracy | Grounded, ↓84% halluc. | Confidently wrong |
| Quality measured | Eval suites | No idea |
| Safety | Guardrails | Unconstrained |
| Cost at scale | Optimized 60%+ | Spirals |
How It WorksA clear path from
A clear path from
start to ship
Disciplined and transparent — weekly visibility, no black boxes, and a working result you can measure.
01
Ground
Index your data and build the retrieval layer that anchors every answer.
02
Build
RAG, prompts, structured outputs, and fine-tuning where it pays.
03
Evaluate
Define golden datasets and evals; benchmark quality and catch regressions.
04
Optimize
Tune accuracy, latency, and cost, then deploy with tracing and monitoring.
Industries
Built for your sector
Enterprise Knowledge
Grounded assistants over internal docs, wikis, and policies — with citations.
RAGSearchCitations
Financial Services
Domain-tuned assistants and extraction under strict accuracy and audit needs.
ExtractionAuditAccuracy
Healthcare
Grounded clinical and ops assistants with guardrails and privacy controls.
GroundingPrivacySafety
Customer Support
RAG assistants that answer from your real docs and escalate cleanly.
SupportRAGEscalation
Developer Platforms
Code- and API-aware assistants tuned to your product and docs.
CodegenDocsAssistant
SaaS Products
Embed reliable LLM features with evals and cost controls built in.
CopilotEvalsCost
Example Outcome
From confident-but-wrong to grounded & measured
Challenge
A company shipped an internal assistant on a raw LLM. It sounded authoritative but was frequently wrong, no one could measure quality, and API costs were climbing fast.
What We Built
A RAG pipeline over their knowledge base, structured outputs with citations, an evaluation suite with a golden dataset, and cost controls via caching and model routing.
Results
84%
fewer hallucinations
0.93
eval score on golden set
61%
lower cost per 1k requests
“For the first time we can measure whether the model is actually good — and it’s grounded in our real docs. The cost drop paid for the project.”
HE
Head of Engineering
Enterprise software
LLM Development ServicesBuild an LLM
Build an LLM
you can trust
Getting an LLM to demo is easy. Getting one reliable in production is the hard part — RAG over your data, fine-tuning where it pays, rigorous evals, guardrails, and cost controls. That’s what we build. Tell us your use case — we reply within 24 hours with a real assessment.
Free consultation24-hour responseNDA on request
FAQ
LLM Development Services questions
If yours is not here, reach out — you get a real answer from an engineer within 24 hours, not a sales pitch.
Usually RAG first: it grounds answers in your data without retraining. Fine-tuning helps for tone, format, or narrow tasks. We recommend based on your case, not defaults.
We build evaluation suites with golden datasets and automated scoring, so quality is measured and regressions are caught before they ship.
OpenAI and Anthropic frontier models, plus open-source (Llama, Mistral) when cost, privacy, or self-hosting calls for it.
Grounding with RAG, structured outputs with citations, validation, and evals — typically cutting hallucinations by 80%+ versus a raw model.
Yes. For sensitive data we deploy open-source models on your own infrastructure, keeping everything in your environment.
A focused, production RAG system is typically 5–10 weeks including evals and cost optimization; fine-tuning adds time depending on data.
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