LLM Integration
Integrates large language models into existing applications back-office systems and data pipelines so the model becomes callable infrastructure rather than a side project.
Everything included under this practice line.
Model gateway and routing layer with fallback across providers for uptime and cost control
SDK and API integration into web apps mobile apps back-office tools and batch pipelines
Streaming response handling token budgeting and rate limit management per tenant
Prompt template library with versioning A/B testing and rollback
Semantic caching and embedding reuse to cut repeat inference cost
Structured output enforcement using JSON schemas function calling or constrained decoding
Secure handling of prompts and completions including redaction encryption in transit and retention policy
The stack we reach for.
What the business gets, measured.
- LLM features shipped into products already in production without rewrites
- Predictable inference cost through routing caching and tier selection
- Reduced vendor lock-in through a gateway that abstracts the model behind it
- Compliance-friendly logging and redaction for regulated workloads
- Faster iteration on prompts because templates are versioned and testable
The specialists behind this practice line.
Backend and platform engineers own the integration surface and gateway while applied AI engineers tune the prompts and structured output contracts. Platform reliability engineers set up the observability and cost monitoring around it.
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.
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.
Let's talk
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