LLM Solutions & Chatbots
We design and deploy production-grade conversational AI systems powered by large language models. From customer-facing chatbots to internal knowledge assistants, we tailor every solution to your business context, data, and compliance requirements.
- → Custom chatbot development for customer support, sales, and internal use
- → RAG-powered assistants grounded in your proprietary data
- → Multi-turn conversation design and context management
- → LLM selection and cost optimization (OpenAI, Anthropic, open-source models)
- → Integration with existing platforms and APIs
- → Evaluation frameworks and continuous quality monitoring
Fine Tuning
Generic models don't always meet the bar. We fine tune language models on your domain-specific data to align behavior, tone, terminology, and output format precisely to your requirements — while managing cost, latency, and compliance trade-offs.
- → Supervised fine tuning (SFT) for domain adaptation
- → Instruction tuning and RLHF pipeline design
- → Dataset preparation, curation, and quality review
- → LoRA and QLoRA efficient fine tuning for cost reduction
- → Evaluation benchmarking pre and post fine tuning
- → Deployment and serving of fine tuned models
AI Pen Testing & Guardrails
AI models introduce new attack surfaces. We combine adversarial red-teaming with CrocoTiger's semantic fencing technology to test your models, identify vulnerabilities, and deploy real-time guardrails that block prompt injection and contextual drift with 99.36% accuracy.
- → LLM red-teaming and adversarial prompt testing
- → Prompt injection and jailbreak vulnerability assessment
- → CrocoTiger integration for real-time semantic fencing
- → Context boundary definition and enforcement
- → Compliance and policy guardrail implementation
- → Continuous monitoring and security reporting
MCP-Centric Development
The Model Context Protocol is the emerging standard for connecting AI agents to tools, data, and services. We architect and build MCP-native systems that enable composable, reliable agent workflows — from single-agent tools to multi-agent orchestration pipelines.
- → MCP server and client development
- → Tool and resource integration for AI agents
- → Multi-agent orchestration and workflow design
- → Claude, GPT, and open-source agent framework integration
- → Context management and memory architecture
- → Testing and evaluation of agentic systems
World Class RAG
Retrieval-Augmented Generation is only as good as its pipeline. We build end-to-end RAG systems engineered for accuracy, speed, and scale — from document ingestion and chunking strategy to embedding selection, vector search, reranking, and response evaluation.
- → Document ingestion pipelines (PDF, web, databases, APIs)
- → Chunking strategy design and optimization
- → Embedding model selection and vector store setup
- → Hybrid search (semantic + keyword) and reranking
- → RAG evaluation: faithfulness, relevance, groundedness
- → Continuous pipeline monitoring and improvement
Data Science & AI Products
We bring 20+ years of applied data science experience to AI product development. From exploratory analysis to production ML pipelines, we help organizations build intelligent products that learn, adapt, and deliver measurable business outcomes.
- → Machine learning model development and deployment
- → Predictive analytics and forecasting systems
- → AI product design and roadmap development
- → Data pipeline engineering and feature stores
- → Model monitoring, drift detection, and retraining
- → Statistical analysis and experiment design
Managed AI Development
Building AI capabilities requires specialized talent that is hard to hire and harder to retain. Our managed services model gives you a dedicated AI engineering team — architects, ML engineers, and data scientists — operating as a seamless extension of your organization.
- → Dedicated AI engineering team under a managed model
- → Full-lifecycle ownership: discovery to production
- → Flexible team scaling based on project needs
- → Transparent reporting and agile delivery cadence
- → Knowledge transfer and internal capability building
- → Predictable costs with no recruiting or retention overhead
AI-Driven Software & Testing
We apply AI to accelerate software development and quality assurance — from intelligent code generation and review to AI-augmented test automation. The result is faster delivery, higher coverage, and engineering teams that spend time on what matters.
- → AI-assisted development with Cursor, Copilot, and Claude Code
- → Automated test generation from requirements and code
- → AI-powered regression and exploratory testing
- → Intelligent code review and vulnerability detection
- → CI/CD pipeline integration with AI quality gates
- → Performance and load testing with AI-driven analysis