Technology Services · LLM Development
Custom LLM development that actually understands your business
Off-the-shelf language models don’t know your industry, your terminology, or how your business operates. We build and adapt large language models that do — through domain-specific fine-tuning, instruction tuning, and retrieval where it fits — so what reaches production is accurate, secure, and built for your environment, not a demo.
What we build
Custom LLM solutions we deliver.
From domain fine-tuning to on-device deployment and multilingual models — LLMs built for your production environment, not a demo.
Expert Team & Proven Experience
10+ years in the industry, with 500+ happy clients worldwide.
Domain-Specific Fine-Tuned LLMs
We handle the full fine-tuning pipeline from data preparation through training, evaluation, and deployment — producing a model that speaks your language from the first inference, whether that’s medical, legal, financial, or your own product terminology.
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Instruction-Tuned & Chat-Optimized Models
Trained on carefully constructed prompt and response pairs so the model interprets requests, structures outputs, and handles ambiguous inputs gracefully — built for customer-facing assistants, internal copilots, and automated processing.
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RAG Combined with Custom LLMs
Hybrid systems that pair a fine-tuned or instruction-tuned model with a retrieval layer pulling relevant content from your knowledge bases at inference time — ideal where information changes often or hallucination risk needs systematic grounding.
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On-Device & Edge LLM Deployment
When sending data to a cloud-based model isn’t acceptable, we build optimized, quantized LLMs sized and validated against your accuracy and latency requirements — so your data never leaves your environment.
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Multilingual LLMs
Built through continued pre-training on multilingual corpora and fine-tuning on domain-specific data across target languages — handling regional terminology, cultural nuance, and language-specific compliance so your model serves every market you operate in.
Code-Focused LLMs
General-purpose models don’t know your codebase, internal libraries, or coding standards. We build code-focused LLMs fine-tuned on your internal codebase and documentation — powering code completion, automated review assistants, documentation generators, and test creation tools that are genuinely useful rather than producing generic suggestions that need rework.
Our approach
We start with whether you need a custom LLM at all
Many use cases are better served by a well-designed RAG system or prompt engineering on a smaller model than by a full fine-tuning project, so before recommending a custom LLM we give you an honest assessment based on your data, budget, and timeline. When a custom model is the right answer, we own the entire pipeline — data preparation, deduplication, PII redaction, training, evaluation, quantization, deployment, and monitoring — so you’re not stitching together work from multiple vendors.
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Compliance & security
Compliance and security built into the pipeline, not added after
Training data is often your most sensitive asset. We detect and redact PII before it enters the training process, store it in encrypted, access-controlled environments, and apply data minimization throughout, producing compliance evidence packs for GDPR, HIPAA, and SOC 2 as applicable. Every model also runs through a custom evaluation framework combining standard benchmarks with test sets from your domain — covering accuracy, format consistency, edge cases, and safety and bias characteristics — so a model is only called production-ready once it has actually proven itself.
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Our process
From discovery to a model you can keep improving
We move from a discovery and data-strategy audit (1–3 weeks) through data preparation and pre-processing (2–4 weeks), sprint-based fine-tuning, alignment, and evaluation, to quantization, staged deployment, and monitoring — backed by 90 days of post-launch support. We’re not tied to any base model, framework, or cloud provider, and this work sits alongside our wider AI and ML practice and our generative AI development team, so a fine-tuned LLM can plug into RAG, agents, or generative workflows as your needs grow.
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What clients achieve with custom LLMs.
What a custom model changes once it’s in production — from usable outputs to independence from external APIs.
Outputs That Are Actually Usable
Generic models use the wrong terminology, follow the wrong structure, and rarely reflect how your business communicates. A domain-fine-tuned model produces accurate, on-brand, structurally consistent outputs from the first generation, reducing the editing burden and making AI-assisted workflows genuinely faster.
Reduced Dependence on External APIs
Self-hosted custom models give you control over cost, latency, data privacy, and availability — no pricing changes, rate limits, or service disruptions from external providers. At scale, the economics of a self-hosted model are typically significantly better than paying per token.
Better Performance on Specialized Tasks
Custom LLMs built and evaluated against your actual tasks consistently outperform general-purpose models on the metrics that matter — whether that’s domain-specific classification accuracy, document generation quality, code suggestion correctness, or output consistency across a multilingual user base.
Compliance Confidence in Regulated Sectors
Organizations in healthcare, finance, and other regulated industries often cannot use third-party AI for sensitive workloads. A custom LLM deployed within your own infrastructure means your data never leaves your environment, every inference is logged, and the system can be audited end to end.
A Foundation for Multiple AI Products
A well-built custom LLM is not just a solution to one problem — it’s a foundational capability that can be extended across multiple products and workflows. We build with reusability in mind so your investment compounds over time rather than solving one problem and sitting idle.
By the numbers
A decade of proven delivery.
10+
Years of proven success
500+
Happy clients worldwide
20+
Products we have built
250+
Technical team members
Technologies we work with
- Open-Weight Foundation Models
- LoRA & QLoRA Fine-Tuning
- RLHF
- Direct Preference Optimization
- DeepSpeed & FSDP
- ONNX Export
- Docker & Kubernetes
- AWS / Azure / GCP
- Vector Databases
- Drift Detection
Related services
Part of our AI development services.
One of 13 specialized practices under our AI & ML hub — explore the ones most relevant to what you’re building.
FAQ
Frequently asked questions
What we hear most often about building, securing, and deploying a custom LLM.
Do we need to build a custom LLM or can we just use an existing API?
It depends on your use case. If general outputs are good enough, your data can go to an external provider, and cost and latency at your expected volume are acceptable, an API-based approach may serve you well. If you need domain-specific accuracy, data privacy, cost control at scale, or on-premises deployment, a custom or fine-tuned model is the better answer.
What data do we need to fine-tune a model?
It depends on what you are trying to achieve. Domain adaptation benefits from large volumes of unlabeled domain text. Instruction fine-tuning requires a smaller set of high-quality prompt and response pairs. Preference optimization requires responses with human preference labels. We audit your available data during discovery and tell you what you have, what you need, and how to bridge any gaps.
How do you ensure our proprietary data is handled securely during training?
All training data goes through PII detection and redaction before entering the pipeline. Data is stored in access-controlled, encrypted environments throughout. We apply data minimization principles and produce compliance documentation covering GDPR, HIPAA, and SOC 2 as applicable. Your data does not leave your designated environment.
How long does a custom LLM project take?
A focused fine-tuning or adapter project typically takes 6 to 14 weeks. A full alignment pipeline with large datasets, multi-stage training, and extensive evaluation typically takes 3 to 8 months. We provide a precise timeline after the discovery and data strategy phase.
Can we combine a custom LLM with RAG?
Yes, and this is often the most effective approach. A fine-tuned model handles domain language and output style while a RAG layer provides access to current, specific information at inference time. We build hybrid systems that integrate both into a single coherent deployment.
What ongoing support do you provide after the model is deployed?
We include 90 days of active post-launch support covering output quality monitoring, drift detection, and performance tuning. After that, ongoing retainers cover base model refreshes, adapter updates, retraining cycles, and evaluation framework maintenance. A custom LLM is a living system and we support it as one.
Ready to build a language model that actually understands your business?
An honest assessment of what’s feasible, what approach fits, and what to expect. No obligation.