AI & Machine Learning Development Services

Adding artificial intelligence to a business workflow should solve an operational bottleneck, not create an expensive demonstration. Many companies experiment with language models only to discover that unvetted API calls leak proprietary data, public models hallucinate critical figures, and fragile prototypes break when handling messy customer input.

At Vasilkoff, we approach AI development as an engineering discipline. We build reliable software around machine learning models: enforcing strict output schemas, securing API keys behind serverless proxies, connecting retrieval pipelines to verified company data, and deploying self-hosted models when privacy regulations prohibit cloud APIs.

Core AI development capabilities

Custom LLM integration and retrieval pipelines

We connect foundation models to your operational data using Retrieval-Augmented Generation (RAG) and the Model Context Protocol (MCP). Rather than letting models invent answers, we restrict responses to verified internal documents, catalog records, and databases.

• Searchable internal knowledge bases from PDFs, meeting notes, and transcripts, covered in our AI meeting knowledge base service.
• Strict JSON-schema output enforcement so model outputs flow directly into downstream databases and business tools.
• Real-time language translation and multilingual workflows that preserve industry-specific terminology.

Self-hosted private LLMs

For legal, financial, and healthcare organisations that cannot send sensitive customer records to third-party cloud APIs, we deploy and fine-tune open-weights models (such as LLaMA, Mistral, and Qwen) on private cloud instances or on-premise hardware. Your team retains complete control over model weights, context data, and infrastructure costs.

Conversational assistants and customer intake

We engineer automated intake systems that handle inquiries across multiple channels:

• Intelligent website chat and messaging bots that resolve common inquiries and collect qualified lead information, detailed under our bilingual AI customer support bot service.
• Multichannel inbox management connecting Instagram, WhatsApp, Telegram, and website forms into a single qualified feed via AI lead qualification and inbox automation.
• Deterministic escalation rules that hand uncertain or complex questions over to human team members with full conversational context.

Secure backend architecture and API protection

Calling AI model endpoints directly from client-side code exposes private API keys to theft and leaves your billing open to scraping. We route AI operations through protected serverless proxies that manage authentication, strict rate limiting, user quotas, and automatic model failover, as detailed in our secure AI backend and API key protection service.

Market research and content workflows

We build automated data collection pipelines that gather sector trends, extract key data points, and prepare reviewable drafts for your team. Our AI trend research and content automation service ensures human editors inspect and approve every publication.

Prototype audit and production stabilization

If your team built an initial micro-SaaS or tool using AI coding assistants, we audit and refactor the code to eliminate security vulnerabilities, broken state management, and runaway model costs. See our vibe-coded app and SaaS rescue and vibe-coded product audit.

How we structure AI engagements

1. Discovery and feasibility review: We evaluate your target workflow, inspect sample data, and determine whether a commercial API or a self-hosted model is the appropriate technical fit.
2. Architecture and validation spike: We establish the data schemas, test prompt reliability against realistic edge cases, and design the fallback mechanisms.
3. Backend and integration development: We write the serverless endpoints, connect your existing database or CMS, and configure observability tools to log model latency, cost, and output accuracy.
4. Testing and human-in-the-loop controls: We verify response consistency, configure rate limits, and test escalation paths to ensure users always have access to human assistance when needed.
5. Deployment and ongoing maintenance: We launch the integration and provide ongoing monitoring to accommodate model updates and changing data structures.

What it costs

AI development work is billed at our transparent flat rate of $39 per hour, or under agreed fixed milestones for defined implementation sprints.

• Proof-of-Concept & API Integration: A focused assistant or data extraction pipeline typically takes two to four weeks of engineering effort.
• Enterprise RAG & Knowledge Systems: Multi-source retrieval systems with custom permissions and authentication usually take six to twelve weeks.
• Self-Hosted Infrastructure: Setting up private inference clusters, quantization, and containerised deployment pipelines is scoped based on your hardware and uptime requirements.

See our pricing page for details on commercial terms, team models, and support retainers.

Proof

Our team has deployed production AI systems that handle real user interactions and enterprise data:

• Vasilkoff.info: Our automated intake tool and client scoping platform, providing instant structured proposals and project scoping through controlled AI dialogs.
• smrtAI: An advanced AI management platform featuring self-hosted language models, strict privacy controls, and fine-tuned domain capabilities.
• CAS.AI: A secure AI application leveraging protected backend endpoints to insulate sensitive operational data from client-facing interfaces.
• Automated Real Estate Sales Workflow: An integrated n8n and OpenAI workflow that parses incoming buyer requests, matches preferences against database listings, and routes qualified leads directly to agents.

Related services

• AI lead qualification and inbox automation connects messaging channels into a single intake flow
• AI meeting knowledge base transforms recorded discussions and transcripts into searchable organizational knowledge
• AI trend research and content automation watches industry sources and prepares reviewable drafts
• Bilingual AI customer support bot provides accurate customer assistance in multiple languages
• Secure AI backend and API key protection moves model credentials into server-side endpoints with rate limiting
• GEO & AI search visibility optimizes your site structure so AI assistants cite your business accurately
• Custom web application development builds the software interfaces where AI features reside
• API development and integration connects models to internal databases and external SaaS tools

Next step

Tell us about the process you want to automate or the feature you want to build. We will review your requirements, recommend the most cost-effective architecture, and provide a clear timeline. Discuss your AI project with our team.