MLOps
Puts machine learning models into production with the pipelines monitoring and governance that keep them accurate and safe once real traffic hits them.
Everything included under this practice line.
Feature store design and offline to online parity for training and serving
Training pipeline orchestration with reproducible runs artifact lineage and experiment tracking
Model registry versioning and promotion workflows across dev staging and production
Deployment patterns including batch real-time streaming shadow and canary
Monitoring for data drift concept drift prediction distribution and business KPI impact
Retraining triggers and automated rollback when quality degrades
Model governance including approval records bias reviews and documentation for audit
CI/CD for models with testing on data slices not just code
The stack we reach for.
What the business gets, measured.
- Models that stay accurate in production instead of quietly decaying
- Reproducible experiments so a promising result can actually be shipped
- Faster iteration from data scientist notebook to production endpoint
- Auditable model lineage for regulated use cases
- Reduced cost of running models through right-sized serving infrastructure
The specialists behind this practice line.
ML platform engineers build the pipelines and serving infrastructure while data scientists and model owners define the evaluation criteria and drift thresholds that matter for each model. Site reliability engineers plug model endpoints into the same monitoring and on-call practices as the rest of production.
Compose several capabilities into one engagement.
Generative AI Solutions
We build production GenAI features on top of Claude GPT-4 and open-weight models. Text image and code generation wired into your product with proper eval loops and cost controls.
AI Agents & Workflow Automation
Multi-step agents that call tools hit your APIs and finish real work. Built with LangGraph or Anthropic's agent SDK with human-in-the-loop checkpoints where the blast radius is real.
LLM Integration
We wire LLMs into existing apps behind a stable API. Provider routing across Anthropic OpenAI Bedrock and self-hosted models so you can swap without rewriting callers.
AI Chatbots & Virtual Assistants
Support and internal-ops bots grounded in your docs and ticket history. Deployed to web Slack Teams or WhatsApp with escalation to a human when confidence drops.
Retrieval-Augmented Generation (RAG)
RAG pipelines over your PDFs wikis and databases. Chunking hybrid search and reranking tuned on your actual queries not a demo dataset.
Intelligent Automation
Replacing rules-based RPA and manual ops work with LLM-driven document parsing classification and routing. We measure the human hours actually saved not the demos.
AI-Powered Business Applications
Full applications where AI is the core feature not a sidebar. Copilots for sales ops finance and legal built on your data with role-based access and audit trails.
Let's talk
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