Training data curation
We assemble instruction and response pairs from your documents, logs and expert input, deduplicate and clean them, and use synthetic data generation carefully where real examples run thin.
Generative AI
LLM development services tuned to your domain.
Our LLM development services adapt language models to the vocabulary, formats and judgment calls your work demands. We fine-tune, evaluate and deploy models that perform your specific tasks well, on infrastructure you control and can audit end to end.
- LoRA and full fine-tuning
- Private deployment options
- Benchmarked on your tasks
01 Overview
Custom language models shaped around your specific workload
General-purpose models are remarkable generalists, but they can be verbose, expensive at volume, unfamiliar with your terminology, or unwilling to follow a strict output format every time. Custom LLM development addresses those gaps by teaching a model your domain directly, often letting a smaller, cheaper model match or beat a larger one on the narrow job you actually need done.
Our LLM development services begin with data: curating, cleaning and labeling examples that represent the task, then building an evaluation set before any training starts. From there we choose the right technique, whether supervised fine-tuning, parameter-efficient methods like LoRA, or preference optimization, and the right base model. We finish with private LLM deployment and serving tuned for throughput, latency and cost.
02 Capabilities
LLM Development & Fine-Tuning capabilities.
End-to-end LLM Development & Fine-Tuning: strategy, engineering and the production operations that keep it working.
Supervised fine-tuning
Full and parameter-efficient fine-tuning with LoRA and QLoRA on models such as Llama, Mistral and Qwen, plus hosted fine-tuning APIs when managed infrastructure fits better.
Preference and alignment tuning
DPO and related preference optimization methods teach models which answers your reviewers prefer, improving tone, refusal behavior and adherence to house rules beyond what instructions achieve.
LLM evaluation frameworks
Task-specific benchmarks, LLM-as-judge scoring calibrated against human ratings, and regression suites let you compare base, tuned and competitor models on evidence rather than impressions.
Private LLM deployment
Serving open-weight models inside your cloud or data center with vLLM, TGI or Triton, including quantization, GPU sizing and autoscaling so sensitive data never leaves your environment.
Model distillation and optimization
Distilling a large model's behavior into a smaller one, then applying quantization and batching, cuts inference cost and latency for high-volume tasks without giving up needed accuracy.
03 How we deliver
From first call to production.
Every LLM Development & Fine-Tuning engagement follows the same five stages, so you see value early and risk stays low.
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01
Discover Typically 1–2 weeks
AI readiness assessment, data audit and use-case discovery. We rank opportunities by business value and feasibility and agree on measurable success criteria.
Output: Roadmap · data audit · success metrics
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02
Prototype Typically 2–4 weeks
A working proof of concept on your real data, scored against an evaluation set — so you see accuracy, latency and cost before committing to a full build.
Output: PoC · evaluation report · build estimate
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03
Build Sprint-based
Production engineering: data pipelines, model training or fine-tuning, RAG retrieval, agent tooling, APIs, guardrails and the user interface your team will actually use.
Output: Production code · APIs · guardrails
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04
Deploy Cloud, on-prem or edge
Secure deployment on AWS, Azure, Google Cloud, your private infrastructure or edge devices, with CI/CD, LLMOps and MLOps pipelines from the first release.
Output: CI/CD · infrastructure as code · rollback
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05
Operate Ongoing
Monitoring, drift detection, retraining and cost optimization keep models accurate and affordable as your data, users and the AI landscape change.
Output: Monitoring · retraining · cost reports
04 Use cases
Where it pays off.
Practical, high-value applications we design and build for clients across industries.
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UC-01
Domain-specific language understanding
Fine-tune a model on clinical, legal or financial text so it handles specialized terminology, abbreviations and reasoning patterns that general models routinely misread.
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UC-02
High-volume classification and extraction
Replace expensive frontier model calls with a compact tuned model that tags, routes or extracts fields from millions of records quickly and consistently.
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UC-03
Brand voice and format consistency
Train a model to write in your established style and produce strictly structured outputs, reducing the prompt length and post-processing needed to keep results on spec.
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UC-04
On-premise AI for sensitive data
Deploy an open-source LLM fine-tuned for internal tasks on infrastructure you control, for organizations whose policies rule out sending data to external APIs.
05 Technology
The stack we use.
Proven, well-supported tools chosen per project for accuracy, cost and long-term maintainability.
Base models
- Llama
- Mistral
- Qwen
- Gemma
- OpenAI GPT fine-tuning
Training
- PyTorch
- Hugging Face Transformers
- PEFT
- TRL
- DeepSpeed
- Axolotl
Serving and tracking
- vLLM
- Text Generation Inference
- NVIDIA Triton
- Weights & Biases
- MLflow
06 Industries
Built for your sector.
We deliver LLM Development & Fine-Tuning for regulated and fast-moving industries alike.
When does fine-tuning an LLM make sense?
Fine-tuning pays off when you need consistent behavior that prompting cannot reliably produce: a strict output format, a specialized vocabulary, a particular tone, or strong performance from a smaller model to reduce cost. If the main need is access to changing facts, retrieval is usually the better first step, and the two can be combined.
How much training data do LLM fine-tuning services require?
It varies with task complexity and technique. Narrow formatting or classification tasks can improve with a modest set of high-quality examples, while broader domain adaptation needs more. Quality matters far more than volume. We start by building an evaluation set, run small experiments, and only expand data collection where results show it helps.
Can you fine-tune open-source models and keep everything private?
Yes. Open-source LLM fine-tuning can run entirely within your cloud account or on your own GPUs, and the resulting weights belong to you. We then deploy the model behind your network controls, so training data, prompts and outputs stay inside your environment. This approach is common where data residency rules apply.
How do you know a custom model is actually better?
We define success metrics and a held-out evaluation set before training begins, then compare the tuned model against the base model and a strong hosted alternative on the same tests. Automated scoring is calibrated against human review on a sample, so improvements reflect what your experts would actually judge as better.
What does it take to keep a fine-tuned LLM current?
We hand over reproducible training pipelines, versioned datasets and an evaluation harness. When your domain changes or a stronger base model is released, you can retrain and compare results with the same process. Production monitoring flags quality drift so retraining decisions are driven by data rather than a calendar.