From LLM solutions and RAG pipelines to AI security and MCP-centric development — every engagement combines AI engineering, security best practices, and managed delivery.
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 knowledge use cases — tailored to your business context and users.
Assistants grounded in your proprietary data via retrieval-augmented generation — accurate, current, and scoped to your organization.
Multi-turn conversation design and context management — built for coherent, goal-directed interactions across complex workflows.
LLM selection across OpenAI, Anthropic, and open-source models — balancing capability, latency, and cost for your specific requirements.
Integration with existing platforms, APIs, and data sources — so AI capabilities fit naturally into the tools your teams already use.
Evaluation frameworks and continuous quality monitoring — measuring accuracy, safety, and performance throughout the system lifecycle.
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.
Domain adaptation via SFT — teaching models your terminology, format, and behavior from curated labeled examples.
Instruction tuning and RLHF pipeline design — aligning model behavior with human preference and organizational policy.
Dataset preparation, curation, and quality review — building high-signal training data that drives accurate fine tuning outcomes.
LoRA and QLoRA efficient fine tuning for cost reduction — training high-quality models without full-parameter overhead.
Rigorous evaluation benchmarking pre and post fine tuning — quantifying improvement across accuracy, tone, and task-specific metrics.
Deployment and serving of fine tuned models — optimized for production latency, throughput, and infrastructure cost.
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.
Adversarial red-teaming and prompt testing against real-world attack vectors — jailbreaks, injections, obfuscation, and policy evasion.
Prompt injection and jailbreak vulnerability assessment — structured analysis of your AI system's attack surface before deployment.
CrocoTiger integration for real-time semantic fencing — 99.36% block rate across all attack datasets at 0.49s average response time.
Context boundary definition and enforcement — ensuring your LLM stays within its intended scope regardless of adversarial input.
Compliance and policy guardrail implementation — configurable rules aligned to your regulatory, legal, and business requirements.
Continuous monitoring and security reporting — ongoing visibility into attack patterns, block rates, and system health post-deployment.
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.
End-to-end MCP server and client development — building the protocol layer that connects agents to your tools and data sources.
Tool and resource integration for AI agents — exposing APIs, databases, and services as MCP-accessible capabilities.
Multi-agent orchestration and workflow design — coordinating specialized agents across complex, multi-step business processes.
Claude, GPT, and open-source agent framework integration — building on the best tools for your use case and infrastructure.
Context management and memory architecture — designing persistent, stateful agent behaviors that learn from prior interactions.
Testing and evaluation of agentic systems — validating reliability, determinism, and correctness under adversarial and edge-case conditions.
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.
Ingestion pipelines for PDF, web, databases, and APIs — processing diverse content sources into a unified, searchable knowledge base.
Chunking strategy design and optimization — balancing retrieval precision, context window usage, and semantic coherence.
Embedding model selection and vector store setup — choosing the right representations and infrastructure for your data scale and latency requirements.
Hybrid search combining semantic and keyword retrieval with reranking — returning the highest-relevance results for any query phrasing.
RAG evaluation across faithfulness, relevance, and groundedness — ensuring your system answers accurately and never fabricates.
Continuous pipeline monitoring and improvement — tracking retrieval quality metrics and keeping the system aligned as data evolves.
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 — from feature engineering and training to production-grade serving infrastructure.
Predictive analytics and forecasting systems — turning historical data into forward-looking signals that drive smarter decisions.
AI product design and roadmap development — translating business goals into intelligent product features with measurable outcomes.
Data pipeline engineering and feature stores — building the infrastructure that keeps models trained on fresh, reliable data.
Model monitoring, drift detection, and retraining — keeping production models accurate as data distributions evolve over time.
Statistical analysis and experiment design — rigorous A/B testing and causal inference to validate AI-driven product decisions.
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 — architects, ML engineers, and data scientists embedded in your delivery process.
Full-lifecycle ownership from discovery to production — accountable for outcomes, not just deliverables.
Flexible team scaling based on project needs — ramp up or down without the overhead of traditional hiring or staffing cycles.
Transparent reporting and agile delivery cadence — regular demos, clear metrics, and no surprises across the engagement.
Knowledge transfer and internal capability building — leaving your organization stronger, not dependent, at the end of every engagement.
Predictable costs with no recruiting or retention overhead — fixed managed rates with clear scope and measurable value delivery.
We apply AI to accelerate software development and quality practices — 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 — accelerating engineering output while maintaining quality and code ownership.
Automated test generation from requirements and code — producing comprehensive, maintainable test suites without manual authoring overhead.
AI-powered regression and exploratory testing — catching issues earlier and covering edge cases that manual testing misses.
Intelligent code review and vulnerability detection — automated analysis that surfaces security issues, anti-patterns, and quality risks before merge.
CI/CD pipeline integration with AI quality gates — blocking regressions and enforcing standards automatically on every commit.
Performance and load testing with AI-driven analysis — identifying bottlenecks and modeling system behavior under production-scale stress.