AI & Automation Solutions

Overview

AI systems should solve real problems, not sit in demos. We build custom AI agents, retrieval-augmented generation (RAG) pipelines, and workflow automation that integrate into your existing stack and deliver measurable results. Whether you need document intelligence that understands your contracts, autonomous lead generation that qualifies prospects, or fine-tuned models trained on your domain, we focus on shipping solutions that scale.

Capabilities in Detail

Custom AI agents handle multi-step tasks—research, summarization, routing—with clear guardrails and human oversight. RAG systems connect your knowledge base to large language models so answers are grounded in your data, not generic training. Document intelligence extracts structured data from PDFs, contracts, and forms. Autonomous lead generation qualifies inbound leads, enriches data, and routes to sales—without manual triage. Workflow automation orchestrates tools, APIs, and human steps into repeatable processes.

Our Approach

We start with the problem, not the model. We map your workflows, identify where AI adds value, and design systems that fail gracefully. We prefer composable architectures: modular agents, pluggable retrievers, and clear separation between logic and data. We test with real inputs, measure latency and accuracy, and iterate until the system behaves reliably in production.

FAQs

How long does a typical RAG system take to build?
Most RAG implementations ship in 4–8 weeks, depending on data volume, chunking strategy, and integration depth. We prioritize a working prototype in the first 2 weeks.

Do you fine-tune models or use prompt engineering?
Both. For most use cases, prompt engineering and RAG deliver strong results without fine-tuning. We recommend fine-tuning when you have large, high-quality datasets and need consistent output formatting or domain-specific terminology.

How do you handle hallucinations?
We ground responses in retrieved context, add citation requirements, and use structured outputs where possible. We also design fallback paths when confidence is low.

What's the difference between RAG and fine-tuning?
RAG retrieves your data at query time and feeds it to the model, so answers stay current and grounded in your sources. Fine-tuning bakes patterns into the model itself for consistent format or tone. Most projects start with RAG and add fine-tuning only when the data and the need justify it.

Can AI agents connect to our internal tools?
Yes. We build the connectors — MCP servers and APIs — that let agents read and act in your real systems, with permissions and audit trails you control. It's the same integration engineering behind our Cowork & Agentic Adoption rollouts.

Is our data used to train public models?
No. We design around providers and configurations that don't train on your data, keep inference in-region where you need it, and document the data flow so it holds up under review.