Designing, building, and running enterprise AI architecture where the harness is the product.
A model takes text in, produces text out, and forgets. Everything that makes AI useful in production — what it sees, what it is allowed to do, how many attempts it gets, and what it remembers — is engineered around it.
We deliver production-ready AI systems that embed directly into your enterprise operations.
Stateless models require stateful engineering — the model is an interchangeable component, the harness is the actual product. We build abstractions that decouple your application from any one LLM provider, enforce strict controls over model visibility and retry limits, and maintain operational state across complex, multi-turn business transactions.
Context is a budget. Every call decides what earns a place in a finite window through retrieval, ordering, compression and exclusion — irrelevant context degrades an answer as reliably as missing context. We build working memory for turn-by-turn retention, session memory for conversation-wide tracking, and durable cross-month memory where provenance travels with the context, filtering out noise to cut latency and cost while raising reasoning accuracy.
Several narrow agents outperform one general assistant, because narrow scope produces testable behavior. We build central coordinators that read incoming requests and route them deterministically to specialized agents, with strict task-in/result-out contracts and isolated tools and knowledge per agent to prevent domain bleed.
Text alone changes nothing — tools are the line between describing work and executing it. We build typed interfaces with declared purposes and validated input constraints, grant access per agent and per tool so every permission is enumerable and auditable, and log every request, result and failure across your systems of record.
Engineering a loop means defining what “done” looks like, what counts as failure, and when to stop — an unbounded loop is not autonomy, it is an outage. We implement explicit Plan → Act → Observe → Revise workflows with hard step counts, token cost caps and explicit exit conditions, backed by automated evaluation suites that run on every code or prompt change.
Five components wrap the model layer, each with a single responsibility.
Working, session and durable memory layers that decide what earns a place in the window.
A central coordinator routing requests to explicit, domain-bound agents.
A provider-agnostic interface, so the underlying LLM can change without breaking the system.
Typed tools, enumerable permissions and full call observability.
Bounded Plan / Act / Observe / Revise cycles with explicit step and cost limits.
Define context budgets, tool boundary specs, agent domain maps and safety policies.
Construct the coordinator, domain routing, persistent memory layers and tool interfaces.
Set up automated evaluation test cases, step/cost boundaries and observe-and-revise loops.
Connect to enterprise systems of record — APIs, databases, documents — and benchmark accuracy against target metrics.
Deploy full system observability for real-time trace tracking across every agent tool call.
Automated daily bank matching across hundreds of corporate accounts.
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Built dynamic personal cycle and wellness algorithm platform.
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