AI-Powered Business Applications
Embeds AI directly into line-of-business applications like CRM ERP HRIS and industry systems so predictions summaries and recommendations show up in the tools people already use.
Everything included under this practice line.
Use-case identification inside CRM ERP HRIS and vertical platforms where AI adds measurable lift
Prediction models for churn lead scoring demand forecasting fraud and next-best-action
Recommendation systems tuned to catalog customer segment and business rules
Native integration into Salesforce SAP Workday Dynamics and ServiceNow through their extension frameworks
Explainability surfaces so users see why a prediction was made and can override it
Feedback capture from user actions to improve models over time
Access controls and data residency handling for AI features on sensitive records
The stack we reach for.
What the business gets, measured.
- Higher conversion and retention through better-timed and better-targeted actions
- Adoption of AI features because they live inside tools staff already open every day
- Reduced revenue leakage from missed renewals fraud or forecast error
- Better data quality as models flag inconsistencies at point of entry
- Measurable lift tied to the business metric each model was built to move
The specialists behind this practice line.
Enterprise application specialists who know the target platform lead the integration working with data scientists who own the model and the metric it is meant to move. Business analysts from the affected function validate that the AI feature fits the actual decision the user is making.
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.
MLOps
The plumbing that keeps models alive in production. Training pipelines model registries feature stores and monitoring for drift and quality regressions on real traffic.
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.
Let's talk
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