Intelligent Synergies Suite: deployable agent infrastructure
Six production modules for retrieval, orchestration, review, edge serving, evaluation and governance.
The parts of an enterprise AI system that are identical across every client, already built and hardened, so your engagement spends its budget on what is actually specific to you.
Teams that have validated a use case and now need it to survive contact with real volume, real auditors and real edge cases.
The six modules
ISS-01 · Retrieval Core
Span-level grounding with citation provenance in the schema, so every claim resolves to the exact passage it came from.
ISS-02 · Agent Graph
Deterministic orchestration with full state replay and rollback. A run that failed can be replayed step by step.
ISS-03 · Reviewer Surface
Human-in-the-loop where it earns its keep, with the context a reviewer needs to decide in seconds rather than minutes.
ISS-04 · Edge Inference
Sub-400ms model serving for line-side and clinical settings where a round trip to a datacentre is not available.
ISS-05 · Eval Harness
Outcome-grade scoring against your cases, not benchmark vanity. Regressions surface before deployment.
ISS-06 · Governance
Access control, retention policy and the audit trail that answers your auditor's third question.
How deployment runs
Fit assessment
Which modules your use case actually needs. Most engagements start with two.
Integration
Connected to your identity, data and logging stack rather than run alongside it.
Evaluation
The Eval Harness is populated with your cases before the system carries any real traffic.
Production handover
Runbooks, dashboards, and the escalation path for the day it misbehaves.
How it's measured
Deployment is measured in weeks rather than quarters because the modules are already built and evaluated. Your metrics come from the Eval Harness, scored on your own cases.
Frequently asked
- Is this a product licence or a service?
- The modules are productized, but they are deployed as part of an engagement rather than sold as self-serve software. You get the integration, the evaluation set and the handover with it.
- Can we take only some modules?
- Yes, and most clients do. Retrieval Core and Reviewer Surface are the common starting pair; Edge Inference only matters if you have a latency or connectivity constraint.
- Does it run in our own environment?
- It can. Deployment target is decided during the fit assessment, and data residency constraints are the main input to that decision.
- How do you prevent silent regressions?
- That is what the Eval Harness is for. Scoring runs against your case set on every change, and a regression blocks deployment rather than surfacing in production.
- What integrations are supported?
- n8n, LangGraph, UiPath and Power Automate are supported directly, and the Agent Graph exposes a standard interface for anything else that can hold a webhook.
Keep reading
Logic at the edge: why latency is the new accuracy
When inference happens 400ms from the event, the model gets to participate in the decision. When it happens six hours later, it gets to write a report.
Ground everything: a practical guide to citation provenance
Citation isn't a UX layer on top of generation. It's the contract. Here's the architecture we use to make every generated sentence traceable.