Generative AI Solutions
Builds generative AI applications that produce text images code or structured output tied to a real business workflow so the model becomes a working part of the product rather than a demo.
Everything included under this practice line.
Use-case discovery and feasibility scoring against model capability data readiness and cost per interaction
Foundation model selection across proprietary and open-weight options based on latency cost and accuracy targets
Prompt engineering and prompt template versioning with structured output schemas and validation
Fine-tuning and instruction tuning on domain data including LoRA and QLoRA for cost-controlled customization
Guardrails for content safety PII redaction jailbreak resistance and refusal behavior appropriate to the use case
Evaluation harness with golden datasets LLM-as-judge scoring and human review loops for regression tracking
Cost and latency instrumentation per prompt per user and per feature with caching and routing to cheaper models where quality holds
The stack we reach for.
What the business gets, measured.
- New product features that were previously blocked by content generation or classification cost
- Lower cost per interaction through model routing and prompt caching
- Reduced regulatory exposure through enforced guardrails and audit logging
- Faster path from prototype to a generative feature customers actually use
- Measurable output quality tracked against a baseline instead of anecdote
The specialists behind this practice line.
Applied AI engineers lead model selection and prompt design with ML researchers engaged when fine-tuning or evaluation methodology needs deeper work. Product designers and domain experts shape the interaction pattern so the generative output fits how people actually use the feature.
Compose several capabilities into one engagement.
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.
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.
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