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AI model and LLM fine-tuning services for more relevant and consistent AI performance
Our AI model fine-tuning services cover every stage of customizing an LLM or foundation model for business use. Each engagement is scoped around a measurable target, such as accuracy on a task, output format compliance, or cost per request, so you can see exactly what the training changed.
Fine-tuning Strategy & Assessment
Before any training, we test the base model on your real tasks and check whether better prompting or retrieval-augmented generation (RAG) would close the gap at lower cost. If fine-tuning is the right call, you get a recommended model, method, data requirement, and the evaluation criteria used to judge success.
Supervised Fine-tuning
We train models on curated input and output pairs drawn from your own work: support replies, reports, classifications, or extractions. Supervised fine-tuning (SFT) is the most direct way to teach a model a specific task, and the quality of those examples matters far more than their volume.
LLM Fine-tuning
We fine-tune open-weight LLMs such as Llama, Mistral, and DeepSeek, and hosted models where the provider supports custom training. The goal is a model that uses your terminology, follows your formats, and handles your edge cases without long prompts. For models built from the ground up, see our LLM development services.
Multi-modal LLM Fine-tuning
We fine-tune vision-language models on paired image and text data for tasks like reading scanned forms, describing product photos, interpreting charts, or answering questions about technical drawings. Each modality needs its own data preparation, so evaluation covers both what the model sees and what it writes.
Domain-specific Fine-tuning
Legal, clinical, financial, and engineering text has its own vocabulary and conventions that general models often get slightly wrong. We train on your domain documents and expert-reviewed examples so outputs read the way your specialists write, with fewer slips on the terms that matter most.
Instruction Fine-tuning
When a model must follow a fixed structure every time, such as valid JSON, a specific report template, or a multi-step workflow, we train it on examples of those instructions being followed correctly. This shortens prompts and reduces the format errors that break downstream systems.
Parameter-efficient Fine-tuning
Methods like LoRA and QLoRA train a small set of adapter weights instead of the full model. That lowers GPU memory and training cost, makes it practical to keep separate adapters for separate tasks on one base model, and lets you swap or roll back an adapter without retraining.
LLM Quantization & Optimization
After training, we compress models to 8-bit or 4-bit precision using methods such as GPTQ, AWQ, or GGUF, then re-test output quality. Smaller models need less memory and respond faster, which lowers serving costs and can make self-hosting practical.
Dataset Preparation for Fine-tuning
Most fine-tuning projects succeed or fail on data. We collect, clean, deduplicate, and format your examples, remove personal data where required, and fill gaps with reviewed synthetic examples. For labeling at scale, our AI data annotation services support the process.
Fine-tuning Evaluation & Benchmarking
We build a held-out test set from your real tasks and score the base model on it first. After training, the fine-tuned model runs against the same set, so you see the actual improvement, any regressions, and the remaining failure cases before anything reaches production.
Hyperparameter Optimization
Learning rate, epochs, batch size, and LoRA rank decide whether a model learns your task or simply memorizes the training set. We run controlled experiments, track each run in tools like Weights & Biases or MLflow, and choose settings based on validation results rather than defaults.
Model Alignment & Behavior Customization
Beyond task accuracy, we shape tone, refusal behavior, and response style using preference-based methods such as DPO, trained on examples your reviewers rank as better or worse. We are also clear about the limits: fine-tuning shapes behavior well, but it is not a reliable way to add frequently changing facts.
Fine-tuned Model Deployment
We package the fine-tuned model for serving with vLLM, TGI, or your cloud platform, expose it through an API, and connect it to your applications. Latency, throughput, and cost are tested under realistic load, and our MLOps consulting team can set up monitoring and retraining pipelines.
AI model & LLM fine-tuning across industries
General models know a little about every industry. Fine-tuning gives them depth in yours. These are typical tasks where a fine-tuned LLM can outperform a prompted general model, because each one depends on sector-specific documents, terminology, and workflows.
Banking & Finance
- Regulatory Filing & Disclosure Drafting
- Compliance Policy Q&A Assistants
- AML Alert Narrative Summarization
- Financial Document Extraction
Education
- Curriculum-Aligned Tutoring Assistants
- Essay Feedback & Rubric Scoring
- Course Content & Quiz Generation
- Student Query Assistants
Healthcare
- Clinical Note Summarization
- Medical Coding Assistance
- Discharge Summary Drafting
- Patient Message Triage
Retail
- Store Associate Knowledge Assistants
- Merchandising Copy Generation
- Customer Query Classification
- Supplier Document Extraction
Logistics
- Shipping Document Extraction
- Delivery Exception Summaries
- Customs Classification Assistance
- Carrier Email Triage
Travel & Tourism
- Itinerary Generation Models
- Booking & Support Assistants
- Review Response Drafting
- Multilingual Guest Communication
Automotive
- Service Manual Q&A Assistants
- Warranty Claim Analysis
- Diagnostic Code Explanation
- Dealer Support Chatbots
Real Estate
- Property Listing Generation
- Lease & Contract Clause Extraction
- Due Diligence Document Summaries
- Buyer & Tenant Chat Assistants
Entertainment
- Script & Synopsis Drafting
- Metadata & Content Tagging
- Subtitle Localization
- Audience Feedback Analysis
Manufacturing
- Maintenance Log Summarization
- SOP & Work Instruction Assistants
- Quality Report Generation
- Technical Document Q&A
Insurance
- Claims Notes Summarization
- Policy Document Q&A
- Underwriting Submission Extraction
- Customer Correspondence Drafting
eCommerce
- Product Attribute Extraction
- Product Description Generation
- Search Query Understanding
- Customer Support Chatbots
A Delivery Record Built Over 20+ Years
750+
Enterprise Clients
1800+
Projects Delivered
ISO 9001 Certified
NASSCOM & STPI Accreditation
NASSCOM & STPI Accreditation
350+
IT Professionals
50+
Countries Served
Base Model Selection
The starting model shapes cost, speed, and quality more than any other decision. We compare open-weight and hosted options on your actual tasks, weighing model size, context length, licence terms, and where the model is allowed to run, before committing to any training.
Transfer Learning Without Forgetting
Training too hard on a narrow dataset can make a model worse at everything else, a problem known as catastrophic forgetting. We mix in general examples where needed and test broader skills alongside your target task, so gains in one area do not cost you capability elsewhere.
Knowledge Distillation
A large model can teach a smaller one. We use a stronger model to generate or grade training examples, then fine-tune a compact model on that output. The result can handle a narrow, high-volume task at a fraction of the inference cost of the larger model.
Distributed Training
Larger models and datasets need more than one GPU. We use DeepSpeed, FSDP, and mixed-precision training to spread workloads across multiple GPUs or nodes, with regular checkpointing so long training runs can resume after an interruption instead of starting over.
Model Versioning & Retraining
Your data and requirements will change after launch. We version datasets, adapters, and evaluation results together, so each retraining cycle is reproducible, comparable with the last one, and easy to roll back if quality drops.
Tech stack supporting our AI model fine-tuning services
These tools cover the full fine-tuning workflow, from training frameworks and PEFT libraries to inference servers and cloud ML platforms. We choose the combination that fits your model, data, and hosting requirements rather than forcing a fixed stack.
PyTorch
TensorFlow
Hugging Face
Scikit-learn
Keras
JAX
spaCy
NLTK
Claude
OpenAI GPT
Gemini
Llama
Mistral
DeepSeek
Hugging Face PEFT
LoRA / QLoRA
DeepSpeed
Weights & Biases
MLflow
Unsloth
vLLM
Triton Inference Server
TGI
Ollama
AWS (Bedrock)
Azure AI Foundry
Vertex AI
Databricks
Honest Fit Assessment
We will tell you when fine-tuning is the wrong tool. Many requests are better solved with prompt changes or RAG, and saying so early saves you a training budget that would not have paid off.
Measured Against a Baseline
Every project starts by scoring the untouched base model on your test set. Results are reported against that baseline, so the improvement from fine-tuning is shown in numbers your team can check, not described in general terms.
Your Data, Your Environment
Training can run inside your own cloud account or on-premise infrastructure when data cannot leave your control. Access, retention, and handling of training data are agreed in writing before any work begins.
Data to Deployment in One Team
Dataset preparation, training, evaluation, and serving are handled by one team, backed by our data engineering practice. Fewer handoffs mean fewer gaps between what was trained and what actually runs in production.
Model and Vendor Flexibility
We work with open-weight and hosted models across AWS, Azure, Google Cloud, and Databricks. For open-weight models, the trained weights and adapters are delivered to you, so you are not locked into a single provider.
Support After Launch
Models drift as products, policies, and user behavior change. We monitor output quality after release and plan retraining cycles with you, so the model keeps pace with your business instead of slowly falling behind it.
Our AI model and LLM fine-tuning process for reliable, high-performance models
Our AI model and LLM fine-tuning process combines strategic planning, quality data preparation, controlled training, rigorous evaluation, and continuous optimization to create models aligned with specific business requirements, measurable outcomes, and real-world performance expectations.
1
Define
We identify business objectives, target outcomes, model limitations, and success metrics to establish a clear and measurable fine-tuning strategy.
2
Prepare
We curate, clean, label, and structure high-quality domain-specific datasets while ensuring consistency, relevance, diversity, and sufficient training examples.
3
Train
We configure training parameters, select suitable fine-tuning techniques, and optimize model performance using controlled, iterative, and reproducible training cycles.
4
Evaluate
We rigorously test the fine-tuned model against predefined metrics, benchmark results, and real-world scenarios to validate meaningful performance improvements.
5
Deploy
We integrate the validated model into production environments, monitor performance continuously, and refine it as requirements, user needs, and data evolve.
Need for Domain-specific Expertise
Fine-tuning can make a general-purpose model more effective when applications require specialized terminology, industry knowledge, or domain-specific communication patterns. If the model struggles with your organization's vocabulary, technical concepts, or workflows, fine-tuning can adapt its behavior to produce relevant responses across recurring business tasks.
Requirement for Consistent Outputs
Consider fine-tuning when your application requires predictable responses, specific formats, consistent terminology, or defined behavioral patterns. Unlike relying solely on prompts, fine-tuning can reinforce desired response structures across repeated interactions. This is valuable for classification, extraction, content generation, and workflows where consistency affects efficiency.
Enough High-quality Training Data
Fine-tuning becomes practical when you have enough high-quality examples of the tasks and behaviors you want the model to learn. Well-structured, diverse, and consistently labeled datasets give stronger training signals. If examples are limited, improving data quality or refining prompts may be the better first step.
Limitations of Prompt Engineering
Prompting may not be enough when reliable results need long, complex instructions or repeated guidance. If teams keep rewriting prompts to hold specific behaviors in place, fine-tuning can encode those patterns into the model itself. Before taking that step, a structured prompt engineering review is often worth doing first.
Cost and Performance Optimization
Fine-tuning can make sense when a smaller or specialized model can handle a recurring task effectively, reducing dependence on larger models for every inference. Organizations should evaluate training costs alongside inference volume, latency, token usage, and infrastructure requirements to determine whether customization can deliver economic advantages.
Defined Evaluation Metrics
Fine-tuning should be considered when success can be measured against clearly defined business and technical objectives. Establishing evaluation criteria before training shows whether customization actually delivers improvement. Metrics may include accuracy, relevance, consistency, latency, cost, or task completion rates.
When RAG Is the Better Fit
If the real problem is missing or frequently changing knowledge, such as product catalogs, policies, or recent documents, retrieval-augmented generation (RAG) is usually the better first step. Fine-tuning changes how a model behaves; RAG changes what it can look up. Many production systems combine both.
Client testimonials and reviews showcasing the value we consistently deliver
Explore how our clients describe their journey with us, reflecting strong collaboration, effective execution, and consistent outcomes delivered across engagements. See how our delivery framework ensures consistency from initiation through to successful completion.
❝
Parity AI
We came to Xicom with an ambitious goal: make institutional-grade financial planning accessible to everyone through AI. Xicom went beyond the brief, blending conversational AI and predictive analytics into an effortless platform. Parity AI is exactly what we envisioned.
❝
RadioFX
Xicom took the time to understand what our stations and listeners actually need, building something tailored rather than generic. Live chat and polls are now core to how we connect with our audience — the app has become a true extension of our on-air presence.
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StudyBuddy
Xicom built exactly what our students needed: a secure space to connect, collaborate, and find tutoring support in one place. Their understanding of student behavior shows in how naturally everything fits together — StudyBuddy is something students genuinely rely on.
❝
BoxxDocks
Xicom helped us bring BoxxDocks to life exactly as we envisioned it. Their team understood the complexity of AI powered real-time asset tracking and delivered a platform that's both powerful and genuinely easy to use.
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TodayFit
We are very happy with their services. I cannot recommend them highly enough. The value, ability, and delivery that we have seen has been superb, and we look forward to continuing this relationship.
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SmartVend AI
Their team built SmartVend from the ground up, combining hardware integration, real-time inventory sync, and a centralized dashboard that completely changed how we operate. We couldn't have asked for a more capable technical partner.
❝
Tap Retail
Partnering with Xicom to develop our Tap Retail app and website was one of the best decisions we made. Their team demonstrated exceptional professionalism, technical expertise, and a deep understanding of our business goals.
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GoGmGo
Partnering with Xicom was a game-changer for us. They didn't just build software, they helped us redefine how we run our business. Their proactive support and deep domain knowledge gave us the confidence to innovate faster.
❝
DENVEN
Working with Xicom has been a transformative experience. Their team quickly understood our challenges and delivered a scalable, intuitive solution. I highly recommend Xicom to any organization seeking future-ready, high-performance digital products.
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Frequently asked questions
What is AI model fine-tuning?
AI model fine-tuning is the process of adapting a pretrained foundation model or LLM to your specific tasks, domain, and data using additional training, so it performs more accurately and consistently for your use case.
What's the difference between fine-tuning and prompt engineering?
Prompt engineering guides a model's output through instructions at inference time. Fine-tuning changes the model itself through additional training, giving more consistent, reliable results without needing complex prompts every time.
Should I use fine-tuning or RAG?
Use RAG when the model needs access to knowledge that changes often or lives in your documents. Use fine-tuning when the model needs to behave differently: follow a format, adopt a tone, or perform a specialized task more accurately. Many production systems use both, with fine-tuning for behavior and RAG for current facts.
What data do I need for LLM fine-tuning?
You need a set of high-quality, task-relevant examples, typically instruction and response pairs. A few hundred carefully reviewed examples can be enough for a narrow task, while broader behavior changes need more. We can assess your existing data and prepare it for training if it isn't ready yet.
How much does LLM fine-tuning cost?
Cost depends on model size, the volume and condition of your training data, the fine-tuning method, and where the model will be hosted. Parameter-efficient methods like LoRA usually cost far less to train than full fine-tuning. We provide a scoped estimate after the initial assessment, once data and success criteria are clear.
How long does fine-tuning a model take?
Timelines vary based on model size, dataset volume, and complexity. Smaller, well-scoped fine-tuning tasks move faster than large-scale or multi-domain projects.
Which models can you fine-tune?
We fine-tune open-weight models such as Llama, Mistral, and DeepSeek, and hosted models where the provider offers custom training through platforms like AWS Bedrock, Azure AI Foundry, or Vertex AI. The right choice depends on your task, data sensitivity, and where the model needs to run.
Is our data safe during fine-tuning?
Training can run in your own cloud account or on-premise environment, so data does not need to leave your control. We agree on data access, retention, and deletion terms before work begins, and can remove personal information from datasets during preparation.
What's included in a fine-tuning engagement?
A typical engagement covers strategy and assessment, dataset preparation, training, evaluation against defined metrics, and deployment of the fine-tuned model into your systems.
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