OUR CAPABILITIES
Intelligence
as a Service.
Six specialized disciplines. One integrated team. We cover the full AI stack — from raw data to production-grade intelligent systems that compound in value over time.
LLM Fine-Tuning
Pre-trained foundation models are generalists. Your business problems are specialists. We extract peak performance from Llama 3.3, Mistral, Qwen, and Gemma using parameter-efficient fine-tuning (LoRA, QLoRA, DoRA) on your proprietary datasets — without the catastrophic forgetting that comes from naive full fine-tuning.
Our data curation pipeline cleans, deduplicates, and formats training data to maximize signal-to-noise. We implement RLHF and DPO alignment stages to ensure outputs match your brand voice, safety requirements, and domain conventions. Final models are benchmarked against GPT-4o and Claude 3.7 on your specific evaluation suite.
Deliverables include the trained model weights, quantized versions (GGUF, GPTQ) for efficient inference, a custom evaluation harness, and a production serving configuration via vLLM or TGI.
TECH STACK
DELIVERABLES
- ✓Trained model weights + quantized variants
- ✓Custom evaluation benchmark suite
- ✓Production vLLM serving config
- ✓Fine-tuning runbook for future retraining
RAG Architecture
Retrieval-Augmented Generation transforms static LLMs into dynamic knowledge systems. We design hybrid retrieval architectures that combine dense vector search with BM25 sparse retrieval, query decomposition, and cross-encoder re-ranking — ensuring your AI answers from verified sources, not hallucinated memory.
Our pipelines handle multimodal documents (PDF, HTML, images, tables) with structure-aware chunking that preserves semantic context. We implement query transformation techniques including HyDE, step-back prompting, and multi-query expansion that dramatically improve retrieval recall on complex questions.
Integration options include SaaS knowledge bases (Confluence, Notion, SharePoint), real-time data streams, and SQL databases with NL-to-SQL translation layers. We benchmark retrieval quality using our proprietary RAGEval framework before every production deployment.
TECH STACK
DELIVERABLES
- ✓End-to-end RAG pipeline codebase
- ✓Retrieval quality benchmark report
- ✓Vector store infrastructure (managed or self-hosted)
- ✓Monitoring dashboard for answer quality drift
AI Agents
The next frontier of enterprise AI is not chatbots — it is autonomous agents that plan, use tools, and execute multi-step workflows with minimal supervision. We build goal-oriented agent systems using the Model Context Protocol (MCP), LangGraph stateful workflows, and CrewAI multi-agent orchestration.
Our agent architectures include persistent memory systems (episodic, semantic, and procedural), deterministic fallback paths for high-stakes decisions, and human-in-the-loop checkpoints that keep humans appropriately informed. We implement circuit breakers and rollback mechanisms for production safety.
Common deployments: autonomous code review and PR generation pipelines, customer success agents that handle 90%+ of tier-one support, research agents that monitor competitive intelligence, and data engineering agents that self-heal broken pipelines.
TECH STACK
DELIVERABLES
- ✓Production agent system with monitoring
- ✓Tool library with unit tests
- ✓Observability dashboard (traces, costs, errors)
- ✓Agent behavior playbook and escalation rules
Computer Vision
From manufacturing defect detection to retail shelf analytics, our computer vision systems process visual information at industrial scale with precision that exceeds human inspection rates. We build on foundation models (SAM 2, CLIP, DINOv2) and specialized architectures (YOLOv10, RT-DETR) depending on your latency and accuracy requirements.
Our training pipelines handle imbalanced datasets, synthetic data augmentation, and active learning loops that continuously improve model performance as new edge cases emerge. We specialize in deploying on constrained hardware — NVIDIA Jetson, Intel Neural Stick, and Coral Edge TPU — achieving real-time inference under 50ms.
Quality assurance includes calibrated confidence scores, uncertainty quantification, and explainable heatmaps (GradCAM, SHAP) that make model decisions auditable for regulated industries.
TECH STACK
DELIVERABLES
- ✓Trained and quantized CV model
- ✓Edge deployment package (TensorRT / ONNX)
- ✓Labeling pipeline and active learning loop
- ✓Inference API with confidence scoring
Data Pipelines
AI systems are only as good as the data that feeds them. We design and implement enterprise-grade data pipelines that transform raw, unstructured business data into curated, AI-ready datasets — handling terabyte-scale volumes with reliability guarantees your business can depend on.
Our pipelines implement schema-on-read architectures with automated data quality validation, PII detection and redaction, and lineage tracking for regulatory compliance. We use medallion architecture (bronze/silver/gold layers) to separate raw ingestion from transformation from feature engineering.
Streaming pipelines built on Kafka and Flink process real-time events with sub-second latency. Batch pipelines on Spark handle historical data backfills. Feature stores using Feast or Tecton serve low-latency features to inference endpoints at production scale.
TECH STACK
DELIVERABLES
- ✓Production pipeline codebase (batch + streaming)
- ✓Data quality test suite
- ✓Monitoring and alerting configuration
- ✓Data catalog and lineage documentation
MLOps
Shipping a model is not the finish line — it is the starting gun. Models degrade, data drifts, and business requirements evolve. Our MLOps platform gives you the infrastructure to continuously monitor, retrain, and redeploy AI systems with the confidence of a software engineering team, not a research lab.
We implement end-to-end experiment tracking with MLflow, automated retraining pipelines triggered by drift detection thresholds, A/B testing frameworks for safe model rollouts, and shadow mode evaluation that runs new models in parallel before promotion. Every model version is reproducible from a single commit.
Our Kubernetes-native inference clusters autoscale from zero to thousands of concurrent requests with sub-100ms cold-start times. We implement multi-region deployments, canary releases, and rollback capabilities — cutting average inference cost by 60% through efficient batching and hardware right-sizing.
TECH STACK
DELIVERABLES
- ✓MLOps platform deployment (self-hosted or cloud)
- ✓Model registry with versioning and lineage
- ✓Automated retraining pipeline
- ✓Inference cluster with autoscaling and monitoring
PRICING PHILOSOPHY
No Packages. No Hourly Rates.
Every enterprise AI challenge is genuinely unique. We scope each engagement from first principles — understanding your data maturity, team capabilities, infrastructure constraints, and business objectives before we discuss a single number.
Our scoping calls are free, no-commitment conversations. You walk away with a clear recommendation — whether that is a $15K prototype or a $2M enterprise partnership — and we walk away with a deep understanding of whether we are the right fit for you.
Get Scoped