HartChain builds AI where you need it: Edge AI for organizations that want small language models and workflow agents next to real work, not only behind a shared remote API. Customer platforms, supply and field operations, finance, learning institutions, and other high-trust settings are reference points, not a limit. We focus on edge and hybrid placement, controlled agents, and training so you can meet latency, data sovereignty, and budget goals with intelligence beside your operations.
Latency, data residency, network quality, and uptime targets decide where models and agents should run. HartChain carries one coherent approach to small language models and agents across your sites, hardens operations, encodes your policies, and trains teams so edge and hybrid AI keeps working through audits and staff change.
How we deliver →Compact, domain-aligned models for summarization, classification, drafting, and Q&A over content you authorize. Deploy from endpoint through rack or edge gateway, with optional controlled use of cloud capacity. Spend tracks real workload instead of a permanently oversized remote model.
Learn more →Workflow agents integrated with your existing applications and approval paths. Controlled access to tools and data, full activity logging, defined escalation to people, and stable operation when the network is slow or uneven.
Learn more →Documentation, instructor-led sessions, and follow-on assistance as infrastructure or policy changes. Your operators receive clear procedures for monitoring, exception handling, and model refresh.
Learn more →The same formula scales: small language models sized for the edge, agents and integration you repeat site to site, training and runbooks so operations stay in your hands.
Align on goals and constraints, deploy with repeatable patterns, hand off with runbooks and clear ownership.
We agree use cases, data rules, residency needs, and how you will measure success. Models and tests stay tied to those requirements from the first release.
We build from shared patterns for edge, hybrid, or restricted setups so each new site is consistent, not custom. Agents get controlled access to tools and data, logging, and human signoff where required.
You receive concise runbooks for monitoring, rollback, cost, and model refresh, with owners on your team. We support you as you add locations, workloads, or new compliance needs.
Inference and automation closer to real work. Controls that match your policies. Teams ready to run SLMs, agents, and refresh cycles day to day.
Edge and hybrid placement puts models and agents nearer to people and devices. Fewer round trips when speed matters, and steadier behavior when the network is thin or uneven.
Data and decisions stay inside the boundaries you set. Agents and models carry logging and policy in the implementation, not only on paper.
Small language model economics follow real load. You scale capacity and schedule updates on your timeline, not a vendor default cadence.
We help you decide where each SLM and agent runs, what data never leaves your boundary, and what each rollout phase must prove before you add sites, teams, or use cases.
Start a conversationEdge AI, small language models, AI agents, training, or an end-to-end program: whatever your scale or stage, you are welcome to reach out. Describe your goal in your own words and add context that helps (setting, timeline, geography, rules you must follow), or keep it short. We read every message and respond with a clear next step.