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LLM Fine-Tuning & Alignment
Adapting large language models to domain-specific tasks through supervised fine-tuning, reinforcement learning from human feedback, and direct preference optimization. We design training pipelines that produce measurable improvements against business-relevant benchmarks. Today, companies are fine-tuning models to write legal briefs in the house style of a specific firm, generate radiology reports that match a hospital’s documentation standards, or handle customer support in domain-specific technical language that off-the-shelf models get wrong. A fine-tuned model can take a general-purpose LLM and make it an expert in your field — understanding your terminology, following your formatting conventions, and producing outputs that require minimal human revision.
SFT
RLHF
DPO
LoRA
QLoRA
Evaluation Suites
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RAG Systems & Knowledge Retrieval
Building retrieval-augmented generation systems that ground model outputs in your data. From embedding pipelines and vector store architecture to hybrid search strategies and reranking, we deliver RAG systems that scale and stay accurate. In practice, this means an insurance company can point a model at decades of policy documents and claims history, and underwriters get instant, cited answers to complex coverage questions that previously required hours of manual research. Law firms use RAG to surface relevant precedents across millions of case files. Internal knowledge bases become genuinely conversational — employees ask questions in plain language and receive answers drawn directly from company documentation, with source citations they can verify.
Vector Stores
Embeddings
Hybrid Search
Chunking
Reranking
Qdrant
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AI Agent Frameworks
Designing and implementing autonomous AI agent systems with tool-calling pipelines, multi-agent orchestration, structured planning, and safety boundaries. Our agent architectures are built for reliability and auditability in production environments. Real-world agent systems today can monitor a company’s infrastructure, diagnose incidents, and execute remediation steps without human intervention. Sales teams deploy agents that research prospects, draft personalized outreach, and schedule follow-ups across CRM and email systems. Software engineering agents navigate entire codebases, plan multi-file changes, run test suites, and iterate on failures autonomously — handling tasks that once required hours of focused developer time in minutes.
Tool Calling
Multi-Agent
Orchestration
Planning
Safety Guards
MCP
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Computer Vision & Perception
Implementing visual AI systems for object detection, image classification, video analytics, and scene understanding. We integrate vision models into production pipelines with real-time inference, edge deployment support, and continuous evaluation. Manufacturing facilities use vision systems to catch defects on production lines at speeds no human inspector can match — identifying hairline cracks, misaligned components, or color inconsistencies in real time. Retail operations deploy camera-based analytics to understand foot traffic, optimize store layouts, and detect inventory gaps on shelves. Agriculture companies mount vision models on drones to survey thousands of acres, identifying crop disease, irrigation failures, and pest damage at the individual plant level.
Object Detection
Classification
Video Analytics
Edge Inference
YOLO
OpenCV
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MLOps & Model Lifecycle
Establishing CI/CD pipelines for machine learning: experiment tracking, model registries, automated evaluation gates, deployment automation, and drift monitoring. We build the operational infrastructure that lets teams ship models with confidence. Without MLOps, organizations end up with models that work in notebooks but fail in production, or quietly degrade as real-world data shifts away from training distributions. A well-built pipeline means a data scientist can push a model update, have it automatically evaluated against baseline metrics, deployed to a canary environment, and promoted to production — all with full traceability. When a model’s accuracy starts drifting, monitoring catches it before customers notice and triggers retraining automatically.
CI/CD for ML
Experiment Tracking
Model Registry
Drift Detection
Prometheus
Grafana
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Edge AI & Embedded Inference
Deploying AI models to resource-constrained environments: on-device inference, model quantization, runtime optimization, and hardware-accelerated execution. We bridge the gap between cloud-trained models and edge deployment reality. Smart home devices now run speech recognition and natural language understanding entirely on-chip, responding in milliseconds without ever sending audio to the cloud. Industrial IoT sensors use edge models to detect equipment anomalies and predict failures before they happen — even in facilities with unreliable network connectivity. Autonomous drones process terrain mapping and obstacle avoidance locally, making split-second navigation decisions that cannot tolerate the latency of a round trip to a data center.
Quantization
TensorRT
ONNX
On-Device
INT8/INT4
Pruning
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NLP & Language Understanding
Building natural language processing pipelines for text classification, named entity recognition, sentiment analysis, summarization, and translation. We deploy NLP systems that handle real-world text at scale with structured evaluation. Financial institutions use NLP to scan thousands of earnings calls, regulatory filings, and news articles daily — extracting sentiment signals and material events that inform trading decisions in near real time. Healthcare organizations process unstructured clinical notes to identify patients at risk of readmission or flag adverse drug interactions buried in free-text records. Customer feedback pipelines automatically categorize and route support tickets, surface emerging product issues from social media mentions, and generate executive summaries from thousands of survey responses.
Classification
NER
Sentiment
Summarization
Translation
Transformers
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Conversational AI & Chatbots
Designing intelligent dialog systems with intent routing, multi-turn context management, personality design, and voice interface integration. Our conversational systems go beyond simple Q&A to deliver natural, context-aware interactions. We can also construct richly detailed interactive profiles of real people — living or deceased — drawing on their writings, voice recordings, videos, and the memories of those who knew them. Where a family might once have relied on an old home video to recall a loved one's voice, these AI-driven profiles preserve personality, humor, and storytelling in a form that invites genuine conversation. A grandchild born years after a grandparent's passing can hear stories told in that person's own cadence, ask follow-up questions, and surface memories no one thought to write down. The technology is still evolving, but even in its current form it offers a degree of continued connection that was simply not possible before.
Dialog Management
Intent Routing
Multi-Turn
Voice
Context Windows
Guardrails