Large Language Model (LLM) Development Services

LLM impact in numbers

30–50%

gain in automation efficiency enabled by LLM adoption

38%

higher conversion rates in retail from use of AI-based personalization

5–15%

reduction in marketing spend enabled by GenAI

Our LLM development services

LLM consulting

Our LLM development company identifies high-impact use cases, estimates project scope, and assesses data maturity. Then, we draw up a structured implementation roadmap with key metrics, milestones, and timelines.

Foundation model selection
IT infrastructure recommendations
Resource and feasibility assessment
PoC development and validation
Security and compliance evaluation

LLM app development

Whether it is AI chatbots, smart assistants, research tools, productivity apps, or search systems, we build solutions for diverse use cases and industries. Our data science experts and ML engineers define the app’s design and choose a base model and optimization techniques.

Data engineering for LLMs
Connecting internal data sources
Designing LLM system strategy
Secure handling of sensitive data
Implementing guardrails to reduce hallucinations

Data engineering for AI/LLM

A grounded LLM needs reliable data to learn from. SoftTeco builds AI-ready data foundations making your data accurate, relevant, and well-structured through data cleaning, normalization, structure, deduplication, and transformation.

Connect data sources to a unified, AI-ready data layer
Build data lakes, warehouses, and vector databases for LLMs
Automate continuous data ingestion and processing
Establish automated data quality and validation checks
Secure sensitive data with access, privacy, and governance controls

LLM integration

We integrate LLMs into existing systems, such as CRM, ERP, and BI tools, without major interruptions to current workflows and with minimal downtime. Our engineers craft controlled data flows, so the model remains reliable, produces reliable outputs, and has low latency.

Data flow and latency design
Building retrieval systems
LLM API integration
Prompt and workflow tuning
Embedding the model into business processes

LLMOps

End-to-end LLMOps pipelines support your models from design through post-deployment. Our experts focus on unique LLM challenges and ensure clear prompt versioning and management, model observability, and consistent retrieval quality.

Building evaluation pipelines
Hallucination detection and mitigation
Tracking latency, cost, and token usage
Managing APIs, model endpoints, and services
Orchestration of RAG pipelines and agent workflows

Maintenance and support

SoftTeco provides post-launch support services for LLM solutions to keep them reliable and high-performing. We track model performance, deploy updates, and troubleshoot to ensure they don’t degrade over time and remain secure and compliant.

Refining system instructions and guardrails
Large language model upgrades
Adding new documents and data sources
Tracking response quality and hallucination rates
Monitoring access control and authentication

LLM use cases we cover

Documents processing

Whether in the legal, banking, or healthcare industry, an LLM can be trained to understand specific terminology, enforce regulatory compliance behavior, and apply contextual reasoning. We build LLMs that:

Review regulatory submissions
Summarize incident reports
Extract financial metrics
Validate invoices
Detect fraud indicators

Internal knowledge management

Tailored large language models centralize fragmented knowledge sources in one place and make them instantly available on request. They ingest and normalize emails, tickets, Word docs, PDFs, databases, and spreadsheets, tag metadata, and remove duplicates. You get:

Automatic document classification
Chat-based Q&A over documents
Semantic search capabilities
Cross-source comparison
Version tracking and change summarization
Role-based answers

Sentiment and risk analysis

Organizations across industries use customized LLMs to identify domain-specific, legally safe, and actionable sentiment. Models help to estimate parameters like churn risks and escalation needs, turning sentiments into decision signals. They tell businesses about:

Brand reputation changes
Market sentiment
Return/refund probability
Customer dissatisfaction
Litigation risk signals
Patient feedback insights

Content creation

Businesses in the ecommerce, marketing, and media domains implement LLMs to tailor their brand voice and generate content that meets their audience’s needs. The algorithms enhance personalization and improve content consistency and quality at scale. Other capabilities include:

Generate training materials
Produce catalog content
Adapt content across languages
Improve content readability and engagement
Adjust content’s tone, style, or complexity
Summarize long documents

Customer support systems

For customer support, LLMs can give accurate, fast, and personalized responses 24/7. They reduce wait times, reduce manual workload, and ensure that answers comply with company policies and communication guidelines. Customized LLMs are capable of:

Multi-language support
Use customer history for personalization
Analyze common complaints
Suggest troubleshooting steps
Identify recurring issues
Generate case notes

Personalized recommendations

LLMs learn customers’ intent, preferences, and behavior, and analyze their order history and context to suggest suitable products and services. Today, with 80% of customers considering the experience the company provides as important as its offerings, LLMs help:

Make context-aware upsell suggestions
Suggest wellness programs
Offer employee-specific training
Send replenishment reminders
Recommend energy-saving actions
Advise on curated investment products

LLM development costs

Custom LLM development can cost from $10,000 to $250,000+. Here, data preparation quality and complexity are the main factors that can take up to 60% of the budget. Other core cost determinants include:

Data volume and quality

Data governance requirements

Foundation model used

Compliance requirements

Use case complexity

Deployment requirements

Number of iterations

Team seniority level

Evaluation requirements

Ongoing maintenance

Get the team cost for your LLM project

Calculate the cost of your custom LLM development team in 4 easy steps. We’ll draw estimates based on the required specialists, additional experts, and service period.

Large language models we use

SoftTeco’s large language model development services include fine-tuning proprietary and open-source foundational LLMs. We help businesses to select the model to fit their needs best.

GPT

GPT-4/GPT-5 fits best when you have medium-sized labeled datasets. It has strong instruction-following priors, solid structured output reliability, and predictable behavior post-training.

Claude

Claude is known for its robust semantic generalization from context. It learns well with knowledge injection via RAG and document-heavy reasoning workflows. Works best when you need a light fine-tuning on small to medium datasets.

Gemini

If your company is already in the Google/Google Cloud ecosystem, Gemini might be a good choice for text reasoning tasks and enterprise knowledge extraction. The LLM scales for enterprise workloads, making it a great fit for analytics systems and internal copilots.

Llama

As an open-source foundation LLM, Llama gives businesses full control over the model lifecycle. It supports parameter-efficient fine-tuning (PEFT) techniques, among which LoRA fine-tuning is the most important. It is great for enterprise and high-volume inference systems.

Mistral

Known for its high performance and reasonable cost, the Mistral model can be largely customized using full fine-tuning and parameter-efficient fine-tuning. It operates under European data privacy and regulatory frameworks and works well for structured tasks and summarization.

Gemma

As a lightweight LLM for experimentation, benchmarking, and custom fine-tuning research, Gemma is relatively compact, accessible, and efficient in training. Fitting startups and SMBs, it struggles with deep enterprise-scale customization and complex reasoning.

Why bigger LLMs aren’t always better

Our experience shows that it’s more important to consider a model’s fit for the specific business tasks than to solely rely on such selection criteria as accuracy, speed, or integration capabilities. Despite its technological sophistication, an ill-suited model might struggle to understand your business context and perform narrow tasks, such as drafting contract summaries or assisting with internal operations.

The right approach is to start with the specific business use case and select the foundation model whose characteristics best fit its requirements. Only then does LLM selection shift from guesswork to a strategic engineering decision that influences how effectively the AI solution is integrated with business data, processes, and governance.

Head of Frontend Department

Igor Maslakoff

LLM projects we delivered

AI-based assistant for hospitality and transit environments

SoftTeco created a virtual assistant for Cisco Webex Desk Pro devices to guide users through audio and video in hotels, airports, and event venues. By training an agentic AI using an LLM and applying the RAG technique, we created a solution that not only provides personalized advice on bookings and schedules but also can take action, create records, and use tools. All generated responses are sent to Heygent, which transforms text to audio and video outputs and presents them via an AI avatar.

Conversational AI assistant for a B2B website

SoftTeco’s team developed a smart chatbot to provide support to corporate website visitors. We used the GPT-4 foundation model and trained it on the company’s data to create an assistant that resembles a SoftTeco employee. Now, the LLM application not only provides 24/7 customer support but also serves as a valuable marketing tool for analyzing user needs. The solution doubled customer service speed, increasing lead generation by 14%.

Vehicle diagnostic platform with AI-powered search

We modernized a unified vehicle diagnostics and repair platform by adding LLM-enhanced search, developing a smart chatbot, and improving the backend and frontend architecture. The upgraded search removes the delays users faced when searching for documentation or navigating technical data. The AI chatbot, in turn, helps resellers and technicians quickly retrieve specific repair steps, wiring information, and diagnostic indicators.

1 / 4

Automate repetitive, language-heavy tasks with LLM development services

Words from our clients

We have been working with SoftTeco for more than 7 years and throughout all this time, they never missed a deadline and always displayed the highest level of professionalism.

VP R&D, Blueprint

Irina Lunin

How SoftTeco delivers distinct value

Profound expertise

With over 8 years of experience in AI development, we understand the impact LLMs can have on businesses of all sizes and across domains. Our experts from a dedicated Data Science and Machine Learning Department advise you on the foundation model, suggest an appropriate fine-tuning methodology, and deliver a solution that fully suits your business context.

Security-first approach

We ensure your enterprise data remains safe during LLM fine-tuning by building compliant training datasets. Aligned with ISO 27001, ISO 42001, and ISO 23894 standards and your industry-specific regulations, we develop custom solutions integrating data governance, adversarial testing, and safety evaluation at every stage.

Proactivity and ownership

Our engineers are genuinely invested in your project success, take initiative, offer improvements, and take responsibility for the outcomes. From noticing technical or business logic issues and addressing them to suggesting alternative solutions and fixes, the team acts more like in-house talent than just external hires.

Redefining LLM development services

With foundation models becoming increasingly capable, business priorities shift from simple LLM integration to ensuring the model works reliably within a complex enterprise environment. In this setting, engineering effort is now redirecting toward data source integration, retrieval strategies, and ensuring that responses are based on trusted business knowledge. The lasting value is created through AI solutions that understand business-specific contexts, integrate with internal systems, and produce outcomes people can rely on.

Head of Frontend Department

Igor Maslakoff

AI technologies behind our LLM development

Machine learning (ML)

As an artificial intelligence (AI) subset, machine learning is used to adapt the foundation model to fit your business processes, policies, and use cases. ML engineers apply supervised learning (e.g., fine-tuning) and reinforcement learning techniques, such as reinforcement learning from human feedback (RLHF), to customize LLM behavior. These optimization methods improve task performance, domain adaptation, and alignment with organizational requirements.

Natural language processing (NLP)

Natural language processing (NLP) focuses on enabling models to understand human language. The technology guides LLM customization, defining what to learn: classification, summarization, or translation. It’s not merely a training method but rather a set of language tasks, data types, and evaluation objectives. ML techniques, in turn, adapt foundation models to solve NLP tasks successfully.

Retrieval-augmented generation (RAG)

Retrieval-augmented generation is a way to customize an LLM using external knowledge at inference time. It finds the external information and generates relevant answers based on it. We use RAG for LLM customization to build domain-specific behavior and reduce hallucinations. The technology ensures that the model uses up-to-date knowledge, is grounded in real documents, and generates contextually aligned outputs.

Our LLM development process

Step 1. Project discovery

Our team assesses your business needs and LLM use cases to determine the right foundation model and draw up an MVP roadmap. We define models’ capabilities, compliance requirements, training data availability, and your IT infrastructure readiness.

Step 2. Data preparation

Whether you need fine-tuning, RAG, or prompt engineering, we collect, clean, and structure relevant documents, datasets, and instructions for each case. During the process, we ensure that the data used for model customization complies with legal, regulatory, and internal policies. This multi-layered process includes sensitive data detection, anonymization/masking, compliance filtering, and more.

Step 3. Model training and fine-tuning

Data engineers and ML experts translate strategy into the required LLM behavior by training it on your business data through continuous pretraining, supervised fine-tuning, preference tuning, or a hybrid model fine-tuning strategy. We also help mitigate hallucinations and model drift that may occur after updates.

Step 4. LLM deployment

During the LLM release, we host it on a GPU server, roll out the APIs, manage inference performance, and integrate it with RAG, tools, and business systems. Then, we monitor the model’s behavior in production, tune its performance, and ensure it can scale.

Step 5. Model maintenance and support

The phase includes bug fixes and prompt adjustments, handling of unexpected outputs, and small, iterative improvements. As the workflows and policies change, your LLM may need ongoing fine-tuning to stay relevant. You can request a model update with cost and effort handled separately within the maintenance plan.

Distinctions we offer for top-quality development

Benefits of custom LLM development

Improved domain accuracy

An LLM that learns from internal documents, guides, and structured data generates reliable answers relevant to your business. It accesses systems directly to retrieve relevant answers, summarizing cases and triggering actions instead of offering ungrounded yet plausible-sounding replies.

Operational efficiency

A custom LLM can reduce operational friction and minimize hallucinations. For employees, it results in fewer repetitive tasks and easier interactions with the system. Businesses gain better productivity per employee, improved automation reliability, and decreased rework.

Cost optimization

Fine-tuned models can handle many requests more efficiently, allowing organizations to scale applications without a proportional rise in staffing or infrastructure. Users don’t need to write long or complex prompts to get accurate results, which means fewer input tokens and lower API or inference costs.

Consistent brand voice

When used for customer service or content generation, LLMs support your company’s voice, tone, and communication standards across channels. It strengthens brand perception and ensures that every response sounds like your business, no matter who the end user is.

Enhanced customer relationships

Large language models can analyze support tickets, reviews, and social media posts to assess satisfaction levels, shifts in brand perception, and churn risk. They support decision-making and allow businesses to address issues before they escalate. 24/7 customer support and personalized recommendations lead to higher sales and better customer experience.

FAQ

Do I need a customized LLM, or is a foundation model enough?

A customized LLM fits businesses that require deep specialization, full control, and unique model capabilities. It can help to analyze domain-specific documents, assist with onboarding and training, and enable multilingual customer support. An existing foundation model suits companies whose use cases rely on public knowledge and do not require business-specific context.

How long does it take to deploy a custom LLM?

Typically, it takes experienced engineers 1–3 weeks to create a POC, 3–8 weeks to develop an MVP, and 2–4 months to build production-grade systems. More complex, enterprise-grade solutions can take over 9 months to build. The core factors that influence the project timelines are data readiness, use case complexity, customization and integration complexity.

Is my data safe when training a custom LLM?

At SoftTeco, we implement strong security controls throughout the LLM customization process to keep your data safe. We adhere to security and quality management standards backed by ISO 9001 and ISO 27001 certifications, best data governance practices, and compliance with industry-specific regulations and standards.

How do you mitigate model hallucinations?

We mitigate model hallucinations by grounding the model’s answers in verified information and limiting its abilities to guess. Our team applies RAG, fine-tunes the model, adds validation layers, and implements guardrails to prevent unsupported claims and to check output quality.

Can you deploy a custom LLM on mobile devices?

Yes, we can deploy a custom LLM on mobile devices. As mobile devices have limited RAM, battery capacity, and CPU performance, LLMs are often optimized for them. The core approaches include on-device deployment of a minimized model, a hybrid architecture (local + cloud models), and cloud-based LLMs, in which the mobile device sends requests only to a server-hosted model.