We build large language model solutions that work in production—not just in demos. Custom LLM development, fine-tuning, RAG implementation, and AI agent development. From strategy through deployment, we deliver enterprise-grade LLM solutions that solve real business problems.
Oleg Kalyta
Founder & AI Lead
Free Discovery
Evaluate use case, recommend approachProof of Concept
Working prototype with benchmarksProduction Ready
Full solution deployed
Your team went above and beyond and built an interesting project in very short time.
LLM Development Challenges We Solve
Here's what blocks most AI projects. We know how to get past these.
Your AI proof-of-concept works, but production deployment stalls
We bridge the gap between demos and production systems. Infrastructure, monitoring, error handling, scale.
LLM outputs are inconsistent or unreliable
Fine-tuning, prompt engineering, and guardrails that ensure consistent, predictable responses.
Data security concerns block AI adoption
On-premise deployment, private cloud, data anonymization—whatever your compliance requires.
API costs are unpredictable or too high
Architecture optimization, caching strategies, model selection that keeps unit economics viable.
Your team lacks LLM expertise
Senior AI engineers who integrate with your workflow and transfer knowledge along the way.
Hallucinations undermine trust in AI outputs
RAG implementation, fact verification, confidence scoring, and human-in-the-loop workflows.
From consulting through deployment and maintenance. We handle the full lifecycle of language model projects.
Before writing code, we figure out whether an LLM is even the right solution. Many companies rush into AI projects without understanding the trade-offs. We evaluate your use case, data readiness, and business objectives to recommend the approach that actually makes sense. Sometimes that means a custom model. Sometimes it means fine-tuning an existing one. Sometimes it means a simpler solution that doesn't require LLMs at all.
Building a language model from scratch is a significant undertaking. It requires substantial compute resources, quality training data, and specialized expertise. We do this when your use case genuinely demands it—proprietary data that can't leave your infrastructure, domain-specific language patterns that general models struggle with, or regulatory requirements that rule out third-party APIs. The result is a model tuned precisely to your business vocabulary and logic.
Most projects don't need a model built from scratch. Fine-tuning takes a foundation model like LLaMA, Mistral, or GPT and adapts it to your specific domain. We handle the data preparation, training process, and evaluation cycles. The model learns your terminology, follows your formatting requirements, and produces outputs that match your quality standards. Faster to deploy, lower cost, and often better results than starting from zero.
Retrieval-Augmented Generation connects LLMs to your actual data. The model doesn't just generate text from its training—it pulls relevant information from your documents, databases, or knowledge bases and grounds its responses in facts. We build RAG pipelines with vector databases, semantic search, and chunking strategies that work for your content type. The result: answers that cite sources and stay current without retraining.
A trained model sitting on a server accomplishes nothing. We integrate LLMs into your existing systems—CRMs, customer service platforms, internal tools, mobile apps. This includes API development, authentication, rate limiting, monitoring, and the infrastructure to handle production traffic. We deploy to your preferred cloud (AWS, Azure, GCP) or on-premise when data sovereignty requires it.
Language models in production need ongoing care. Performance drifts. New edge cases emerge. Costs creep up. We provide continuous monitoring, prompt optimization, and model updates that keep your LLM solution performing as expected. This includes tracking hallucination rates, response latency, cost per query, and user satisfaction metrics. When issues arise, we catch them before your users do.
Autonomous AI agents that don't just respond—they reason, plan, and execute. We build agentic systems that decompose complex tasks, use tools, browse the web, interact with APIs, and make decisions based on context. From customer service agents that resolve issues end-to-end to research assistants that synthesize information across sources, we create AI agents with proper guardrails, human oversight checkpoints, and clear audit trails for enterprise deployment.
Autonomous AI systems that reason, plan, and execute. The next evolution beyond chatbots.
Task decomposition and planning
AI agents that break complex goals into manageable steps. They understand your objective, identify what needs to happen, and execute sequentially or in parallel. When something fails, they adapt. This is how agents handle requests like 'research competitors and summarize findings' or 'process this batch of invoices.'
Tool use and API integration
Modern AI agents don't just generate text—they take action. They call APIs, query databases, search the web, send emails, update CRMs. We build agents that know which tool to use when, handle authentication, manage rate limits, and gracefully recover from errors.
Multi-agent orchestration
Complex workflows benefit from specialized agents working together. A research agent gathers information, an analysis agent processes it, a writing agent creates the output. We design multi-agent systems where each agent has clear responsibilities and they coordinate efficiently.
Guardrails and human oversight
Autonomous doesn't mean unsupervised. We implement confidence thresholds, approval workflows for high-stakes actions, cost limits, and audit logging. Agents know when to escalate to humans. Enterprise deployment requires these controls—we build them in from the start.
Memory and context management
Agents need to remember. Short-term memory for ongoing tasks, long-term memory for user preferences and past interactions. We implement retrieval systems that give agents relevant context without overwhelming token limits. The result: agents that feel consistent across sessions.
Real projects, measurable results. AI solutions that made it to production and stayed there.
AI Support Agent
AI Agent Development
Autonomous Customer Service Agent
Built an AI-powered support agent that handles tier-1 customer queries autonomously—understanding context, accessing knowledge bases, processing refunds, and escalating appropriately. The large language model solution reduced support ticket volume by 70% while maintaining customer satisfaction. This is what enterprise-grade AI agent development looks like.
Discover case study
Healify
Custom LLM Development
AI Health Companion
Developed a domain-specific LLM solution for healthcare—an AI health companion that provides personalized guidance based on user data. The system uses retrieval-augmented generation (RAG) to ground responses in medical knowledge, maintains HIPAA-compliant data handling, and knows when to escalate to human professionals. Client raised $2M in funding.
Discover case study
Most clients start unsure whether they need fine-tuning, RAG, or something else. That's what the discovery phase is for.
Different problems require different approaches. Here are the types of LLM systems we develop.
Enterprise AI Assistants
Internal tools that help employees work faster. Document Q&A systems that find information across thousands of PDFs. Meeting summarizers that turn hours of calls into actionable notes. Code review assistants that catch bugs and suggest improvements. We build AI assistants that integrate with your existing workflows—Slack, Teams, email, custom portals—rather than requiring people to switch to new tools.
Customer Service Automation
LLMs that handle support queries without making customers feel like they're talking to a bot. We build systems that understand intent, access your knowledge base, escalate appropriately, and maintain conversation context. The goal isn't replacing human agents—it's handling the repetitive queries so your team can focus on complex issues. Integrates with Zendesk, Intercom, Freshdesk, or custom ticketing systems.
Content Generation Systems
Automated content that doesn't sound automated. Product descriptions at scale. Personalized marketing copy. Technical documentation from code. We fine-tune models on your brand voice, train them on your style guides, and build guardrails that prevent off-brand outputs. Human review workflows catch the rare miss. The economics work when you need hundreds or thousands of pieces, not one-off creative work.
Data Extraction & Analysis
Turning unstructured data into structured insights. Contracts analyzed for key terms. Resumes parsed into standardized fields. Research papers summarized with key findings highlighted. Medical records coded for billing. We build extraction pipelines that handle messy real-world documents, validate outputs, and flag confidence levels. Human review for edge cases keeps accuracy high.
Domain-Specific Language Models
General-purpose LLMs struggle with specialized terminology. Legal language, medical jargon, financial regulations, engineering specifications—these require models trained on domain-specific corpora. We build or fine-tune models that understand your industry's vocabulary, follow its conventions, and produce outputs that professionals actually trust. Not a generic chatbot wearing a costume.
AI Agent Development
LLMs that don't just generate text—they take action. Agents that research topics across the web, execute multi-step workflows, interact with APIs, and make decisions based on context. We build agentic systems with proper guardrails, human-in-the-loop checkpoints, and clear audit trails. Useful for complex tasks where the steps vary based on what's discovered along the way.
Transparent pricing based on project scope and complexity.
Here's what typical ML initiatives cost based on projects we've delivered.
What most impressed me about ProductCrafters was their dedication to my project and understanding of our goals. They were very honest and transparent throughout the entire process.
They were flexible, and it was easy to work with them on a day-to-day basis. Their brilliant ideas were critical to the project success.
Out of over 40 applicants, we selected ProductCrafters based on their experience, technical expertise, and cost estimate. The team showed deep technical expertise, a strong work ethic, and honesty.
The team has honest billing practices and creates incredible value for the cost. Working with ProductCrafters has saved us hundreds of thousands of dollars compared to domestic firms.
The quality of their code makes them a valuable partner. They thought holistically about solutions and brought up all-encompassing ideas.
Their insightful advice has maximized the application's performance. We're actually learning things from ProductCrafters that we can adapt and use in other applications.
What most impressed me about ProductCrafters was their dedication to my project and understanding of our goals. They were very honest and transparent throughout the entire process.
They were flexible, and it was easy to work with them on a day-to-day basis. Their brilliant ideas were critical to the project success.
Out of over 40 applicants, we selected ProductCrafters based on their experience, technical expertise, and cost estimate. The team showed deep technical expertise, a strong work ethic, and honesty.
The team has honest billing practices and creates incredible value for the cost. Working with ProductCrafters has saved us hundreds of thousands of dollars compared to domestic firms.
The quality of their code makes them a valuable partner. They thought holistically about solutions and brought up all-encompassing ideas.
Their insightful advice has maximized the application's performance. We're actually learning things from ProductCrafters that we can adapt and use in other applications.
We work with leading foundation models, frameworks, and infrastructure. The right tools for your specific requirements.
AI & ML
OpenAI GPT
Hugging Face
PyTorch
TensorFlow
OpenAI GPT
Hugging Face
PyTorch
TensorFlow
OpenAI GPT
Hugging Face
PyTorch
TensorFlow
OpenAI GPT
Hugging Face
PyTorch
TensorFlow
OpenAI GPT
Hugging Face
PyTorch
TensorFlow
OpenAI GPT
Hugging Face
PyTorch
TensorFlow
Backend
Python
FastAPI
Node.js
PostgreSQL
Python
FastAPI
Node.js
PostgreSQL
Python
FastAPI
Node.js
PostgreSQL
Python
FastAPI
Node.js
PostgreSQL
Python
FastAPI
Node.js
PostgreSQL
Python
FastAPI
Node.js
PostgreSQL
Cloud & Infrastructure
AWS
GCP
Docker
Kubernetes
AWS
GCP
Docker
Kubernetes
AWS
GCP
Docker
Kubernetes
AWS
GCP
Docker
Kubernetes
AWS
GCP
Docker
Kubernetes
AWS
GCP
Docker
Kubernetes
Frontend
React
Next.js
TypeScript
GraphQL
React
Next.js
TypeScript
GraphQL
React
Next.js
TypeScript
GraphQL
React
Next.js
TypeScript
GraphQL
React
Next.js
TypeScript
GraphQL
React
Next.js
TypeScript
GraphQL
LLM applications vary dramatically by industry. Domain knowledge matters as much as technical skill.
Healthcare & Life Sciences
Clinical documentation automation, patient communication systems, medical coding assistance, and drug interaction analysis. Healthcare LLM development requires HIPAA compliance, careful handling of protected health information (PHI), and outputs that clinicians trust. We've built AI systems that process medical records, answer patient questions with appropriate disclaimers, and support diagnostic workflows—all with human oversight and proper guardrails. Our healthcare AI projects have helped clients raise $2M+ in funding and process thousands of patient interactions daily.
Financial Services
Document analysis for underwriting, customer service automation, regulatory compliance monitoring, and fraud detection narrative generation. Financial services LLM solutions demand complete audit trails, explainability for regulators, and strict data handling protocols. We build large language model solutions that work within these constraints—no black boxes, full traceability, and outputs that compliance teams can review and approve. Our financial AI implementations have reduced document processing time by 80% while maintaining accuracy standards.
Legal
Contract review automation, legal research assistance, document drafting support, and due diligence acceleration. Legal language models need to understand precedent, jurisdiction-specific requirements, and the serious consequences of errors. We fine-tune models on legal corpora, implement citation verification systems, and build confidence thresholds that prevent embarrassing mistakes. Our legal LLM solutions help firms process contracts 10x faster while flagging risk areas for human review.
E-commerce & Retail
Product description generation at scale, customer service chatbots, personalized recommendation engines, and search optimization. Retail LLM applications need to handle catalog scale (thousands of SKUs), maintain consistent brand voice, and drive measurable conversion improvements. We build generative AI systems that create unique, SEO-optimized product descriptions without sounding robotic—helping brands scale content production while maintaining quality.
Technology & SaaS
Developer tooling, code generation assistants, documentation automation, and customer support scaling. Tech companies often have the data sophistication to leverage LLMs effectively but lack specialized ML engineering expertise to build production-grade systems. We bridge that gap—turning AI experiments into reliable features. Our AI support agent reduced response times from 4 hours to under 1 minute while cutting support costs to $0.02 per conversation.
Manufacturing & Logistics
Technical documentation search, predictive maintenance explanations, quality report generation, and supply chain communication automation. Manufacturing LLMs need to understand technical specifications, safety requirements, and operational constraints specific to industrial environments. We build natural language processing systems that help engineers find information faster, generate compliance documentation, and communicate more effectively across global teams.
Honest pricing based on real projects. These ranges reflect what quality LLM development actually costs.
Fine-Tuning Projects
Domain-specific applications, consistent output requirements
$15,000 – $35,000
4-8 weeks
Adapting existing foundation models to your specific domain, terminology, or output requirements. Best fit when you need consistent style or specialized knowledge without building from scratch.
RAG Implementation
Document Q&A, knowledge bases, customer support
$25,000 – $50,000
6-12 weeks
Building retrieval-augmented generation systems that connect LLMs to your knowledge base. Includes vector database setup, chunking optimization, and production-grade retrieval pipelines.
Custom LLM Solution
Complex AI products, enterprise deployments
$50,000 – $100,000+
3-6 months
End-to-end LLM application development including architecture design, model selection/training, full-stack development, and production deployment with ongoing support.
Start with a free discovery week. We'll evaluate your use case, recommend an approach, and provide realistic estimates—before you commit to anything.
LLM projects have a high failure rate. Most never make it to production. Here's why our projects do.
Production LLM experience, not just experiments
We've deployed large language model solutions that handle real traffic, real edge cases, and real user expectations. The gap between an AI demo and a production-grade LLM system is enormous—monitoring costs, handling failures gracefully, managing prompt versioning, and building the LLMOps infrastructure. Our AI support agent handles thousands of queries daily. We bridge the demo-to-production gap because we've done it repeatedly.
Honest LLM consulting—what works and what doesn't
The generative AI hype cycle creates unrealistic expectations. We tell you when a large language model is overkill, when fine-tuning beats RAG, when you need a rules engine instead of natural language processing. No upselling complexity. Our LLM consulting approach focuses on the solution that actually works for your use case. Sometimes the right answer is 'you don't need an LLM for this.'
Enterprise-grade security and data privacy
Your data is your competitive advantage. We build enterprise LLM solutions where sensitive information never leaves your infrastructure when required. On-premise deployments with open-source models like LLaMA and Mistral, private cloud setups, data anonymization pipelines—whatever your compliance requirements demand.
LLM cost optimization from day one
LLM API costs get expensive fast at scale—we've seen projects hit $50K/month. We design architectures that minimize token usage through intelligent prompt engineering, cache responses appropriately, route queries to cost-effective model tiers, and avoid cost surprises. Our AI support agent runs at $0.02 per conversation—that's deliberate architecture, not accident.
LLM development requires a specific combination of skills. Here's what sets us apart.
AI products in production, not just experiments
We've built AI systems that serve real users at scale. Healify raised $2M on the back of our work. Our AI support agent handles thousands of queries daily. The gap between an impressive demo and a reliable production system is enormous—we know how to bridge it.
Full-stack capability, not just ML expertise
LLMs don't exist in isolation. They need APIs, frontends, databases, monitoring, and integration with existing systems. We handle the entire stack—from model fine-tuning to the user interface. One team, one point of accountability.
Honest about limitations
We'll tell you when an LLM isn't the answer. When rules-based logic would be cheaper and more reliable. When the technology isn't mature enough for your use case. Our job is solving your problem, not selling you AI projects.
Cost-conscious architecture
LLM costs spiral quickly at scale. We design systems that minimize API calls, cache intelligently, route to appropriate model tiers, and give you visibility into unit economics before you commit to production.
Recognition
Costs depend heavily on the approach. Fine-tuning an existing model for your domain typically runs $15,000-$35,000. Building a RAG system to connect an LLM to your knowledge base ranges from $25,000-$50,000. Full custom LLM applications with end-to-end development cost $50,000-$100,000+. The main cost drivers are data preparation requirements, integration complexity, and accuracy requirements. We provide detailed estimates after understanding your specific use case—no ballpark figures that turn into surprises later.
AI agent development services help businesses build autonomous systems that can reason, plan, and execute multi-step tasks. Unlike simple chatbots, AI agents use LLMs to understand goals, break them into subtasks, use external tools (APIs, databases, browsers), and make decisions based on context. Examples include customer service agents that resolve issues end-to-end, research assistants that synthesize information across sources, and workflow automation agents that handle complex business processes. The development involves architecture design, tool integration, guardrail implementation, and extensive testing for reliability.
Fine-tuning teaches a model new patterns by training it on your data—it changes the model's weights. The knowledge becomes part of the model. RAG (Retrieval-Augmented Generation) keeps the base model unchanged but connects it to an external knowledge base at query time. Fine-tuning works best for learning styles, formats, or domain-specific language. RAG works best when you need current information, citations, or your knowledge base changes frequently. Many production systems use both.
Timeline varies by project type. Fine-tuning projects typically take 4-8 weeks from data preparation through deployment. RAG implementations run 6-12 weeks including knowledge base setup, retrieval optimization, and integration. Complex custom LLM applications can take 3-6 months for full development. The discovery phase takes 1-2 weeks regardless—that's when we determine the right approach and provide accurate timeline estimates for your specific situation.
Yes. When data sovereignty or regulatory requirements prohibit sending data to external APIs, we deploy models on your infrastructure. This works with open-source models like LLaMA or Mistral that don't require external API calls. On-premise deployment requires more compute resources but gives you complete control over your data. We also implement hybrid approaches where sensitive operations happen on-premise while less critical queries use cloud APIs.
Hallucination mitigation is built into our development process. For RAG systems, we implement source verification and confidence scoring—the model only answers from retrieved documents. For fine-tuned models, we use training data quality controls and output validation. All production systems include monitoring for hallucination patterns, human-in-the-loop workflows for high-stakes outputs, and graceful fallbacks when the model isn't confident. Zero hallucination is impossible, but acceptable error rates are achievable.
We work with both proprietary and open-source models depending on your requirements. Proprietary options include GPT-4, Claude, and Gemini—these offer strong performance but require API calls. Open-source options include LLaMA, Mistral, and Falcon—these can be deployed on your infrastructure. Model selection depends on your accuracy requirements, latency constraints, cost targets, and data sensitivity. We often recommend starting with a proprietary model for faster iteration, then moving to open-source if economics or privacy require it.
Yes. LLMs in production need ongoing attention. Models drift, edge cases emerge, and costs need optimization. Our maintenance includes monitoring for quality degradation, prompt optimization based on real usage, model updates as better options become available, and cost management as query patterns evolve. Most clients continue working with us after launch because they need the system to improve over time, not just maintain the status quo.
Security is built into our development process, not bolted on later. This includes input validation to prevent prompt injection, output filtering to prevent data leakage, API authentication and rate limiting, audit logging of all queries and responses, and encryption at rest and in transit. For sensitive deployments, we implement additional measures: private cloud or on-premise deployment, data anonymization pipelines, and compliance-specific controls (HIPAA, SOC 2, GDPR). Security requirements are defined in the discovery phase and inform architecture decisions.
ROI depends entirely on the use case. Customer service automation typically shows 40-70% reduction in ticket volume—calculate your cost per ticket to find the savings. Content generation at scale might enable 10x output without additional headcount. Document processing automation can cut review time by 80%. We help quantify expected ROI during the discovery phase by understanding your current costs and realistic performance expectations. Not every use case has positive ROI—we'll tell you if yours doesn't.
LLMOps (Large Language Model Operations) is the practice of managing LLMs in production—monitoring performance, optimizing costs, updating prompts, and maintaining quality over time. It matters because LLMs don't stay static: model drift occurs, edge cases emerge, API costs can spiral, and user needs evolve. Without proper LLMOps, your AI solution degrades. Our LLM development services include setting up monitoring dashboards, cost alerts, A/B testing for prompts, and automated quality checks that catch issues before users do.
Domain-specific LLM development starts with understanding your industry's unique vocabulary, compliance requirements, and error tolerance. For healthcare, that means HIPAA compliance and medical terminology. For legal, it means citation accuracy and jurisdiction awareness. For finance, it means audit trails and regulatory language. We either fine-tune existing models on your domain data or implement RAG systems that connect to your specialized knowledge bases. The goal is an LLM that speaks your industry's language and understands its constraints—not a generic model wearing a costume.
A good LLM development company has three things: production experience (not just demos), honest communication about what LLMs can and can't do, and full-stack capability to handle everything from model selection to deployment. Watch out for firms that oversell AI capabilities, can't show real production deployments, or treat every problem like it needs a custom model. The best LLM development companies will sometimes tell you that you don't need an LLM at all—that a simpler solution would work better. We've turned down projects where simpler approaches made more sense.
Meet your team
Slack channel, assigned developer, daily standups. First code committed to your GitHub.Working prototype delivered
Technical spike or prototype complete. Architecture + budget roadmap for the full build.You keep everything. Zero cost. Zero commitment.
Oleg Kalyta
Founder & AI LeadLLM development services help businesses build, customize, and deploy large language model solutions. This includes everything from fine-tuning existing foundation models (GPT-4, Claude, LLaMA, Mistral) for domain-specific tasks to building complete RAG (Retrieval-Augmented Generation) systems, developing autonomous AI agents, and integrating natural language processing capabilities into existing enterprise software. Professional LLM development goes beyond API calls—it requires expertise in prompt engineering, model optimization, data preparation, and building production-grade infrastructure that handles real-world scale and edge cases.
Building or fine-tuning language models specifically for your domain, terminology, and use cases. Not generic chatbots—AI that understands your business.
Connecting LLMs to your actual data through retrieval-augmented generation. Answers grounded in facts, with citations, that stay current without retraining.
Autonomous systems that reason, plan, and execute multi-step tasks. Agents that use tools, call APIs, and make decisions—not just generate text.
The infrastructure, monitoring, and optimization that keeps LLM solutions running reliably at scale. Because a demo isn't a product.
Building for a large organization? Enterprise LLM projects have additional requirements. Here's how we handle them.
Data security and compliance
Enterprise data can't just flow to external APIs. We implement on-premise deployment, private cloud setups, data anonymization, and audit trails that satisfy compliance teams. HIPAA, SOC 2, GDPR—we've worked within these frameworks.
Integration with existing systems
Enterprise environments have Salesforce, SAP, ServiceNow, custom internal tools. LLM solutions need to fit into existing workflows, not require people to switch to new systems. We build integrations that feel native.
Scalable infrastructure
Enterprise usage patterns are unpredictable. Marketing campaign hits, quarter-end rushes, global rollouts. We architect for variable load with appropriate caching, load balancing, and cost controls.
Change management support
Technology is the easy part. Getting thousands of employees to actually use new AI tools requires training, documentation, and rollout planning. We support the full adoption lifecycle, not just the deployment.
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