Retrieval-Augmented Generation (RAG)
Grounds LLM answers in your own documents tickets wikis and databases so responses cite source material and stop hallucinating on domain-specific questions.
Everything included under this practice line.
Corpus discovery and content quality assessment against retrieval suitability
Chunking strategy tuned to document type including headings tables and code blocks
Embedding model selection and evaluation against domain vocabulary
Hybrid retrieval combining dense vector search lexical BM25 and metadata filtering
Reranking and query rewriting to lift precision on ambiguous questions
Citation and source attribution so every answer links back to the passage it came from
Freshness pipelines that reindex on document change and expire stale content
Evaluation using retrieval metrics answer faithfulness scoring and question sets from real users
The stack we reach for.
What the business gets, measured.
- Accurate answers on internal knowledge that generic models cannot produce
- Reduced hallucination risk in regulated content areas through enforced citations
- Faster onboarding because new hires can query the corpus directly
- Lower cost than fine-tuning for knowledge that changes frequently
- Better decisions from staff who now have answers they used to hunt for
The specialists behind this practice line.
AI engineers and information retrieval specialists tune the retrieval pipeline to your specific content structure and domain vocabulary working directly with the content owners who know which documents are authoritative. Data engineers set up the ingestion and reindexing so the corpus stays current.
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.
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.
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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