Everyone can sell a strategy deck. Almost nobody closes the gap between “demo worked” and “runs safely with real customers, real compliance, real data.” That’s our wedge.
GJEF Specials is building for the production layer of this wave: governed agents on trustworthy data for mid-market enterprises.
Roughly half of agentic initiatives remain in PoC or pilot. One in three cite no clear business case. Few can tie AI value to P&L.
Illustrative funnel from industry research (Dynatrace, Forrester, Gartner). Failure is often unglamorous: poor data in, brittle APIs, no event-driven design — not “the model is dumb.”
Everything thrown into the prompt. Agents fabricate when the data they’re fed is noisy, incomplete, or contradictory.
Demo APIs that collapse under real systems, rate limits, and exception paths no one modelled.
Without event-driven architecture, agents can’t react reliably to the business as it actually moves.
Missing audit trails, policy constraints, and human oversight — so sign-off never arrives.
Agents fail in production when the data they consume is incomplete, inconsistent, or ungoverned, not only when the model is weak. This short session walks through what “AI-ready data” actually means before you scale autonomy.
GJEF Specials · AI Readiness: Can your Data Power an AI System?
We focus on mid-market clients who already tried an agent pilot — internal team, agency, or big consultancy and it stalled, hallucinated, or never got board sign-off.
The fix is not another roadmap. It’s the data-engineering backbone: clean inputs, governed tools, policy-constrained agents, and observability that survive real customers and real compliance.
Discuss a rescue engagementAn internal pilot could complete demo scenarios but failed board review: inconsistent outputs, weak lineage, and no clear owner for failures in production.
GJEF Specials rebuilt the data path, constrained tool use with policy, added audit logging and human-in-the-loop gates, and redeployed a single high-value workflow against live systems.
Result: supervised production in 11 weeks, with measurable drop in manual exceptions and a path to expand only after controls proved stable.
Agentic risk is not only about what a model says — it is about what an agent does: tools, identity, memory, and side effects on live systems. We design rescues so policy sits below the agent boundary: the agent proposes; the control plane decides.
Every agent is a non-human identity with an owner, scoped credentials, declared purpose, and a decommission path not a shared service account.
Permissions match the current task, not a permanent wide mandate. High-impact actions require human gates and audit trails boards can trust.
Deterministic gateways mediate tool calls. The agent cannot grant itself authority or bypass controls by rewriting its own plan.
Runtime traces of plans, tool invocations, and outcomes so failures are diagnosable and compliance is evidence-based, not assumed.
Common pilot failures — context dumping, brittle APIs, missing event-driven design, weak board-level risk controls map directly to agentic threat classes (tool misuse, identity abuse, memory/context integrity). Rescue work fixes the data and control plane first; model quality alone does not.
Final pricing depends on systems, data readiness, and compliance surface. These ranges orient the conversation.
Strategy and readiness assessments: diagnose the stalled pilot, data quality, integration risk, and board-ready path.
Full production deployment for a single agentic workflow governed, observable, and built to survive real load.
Enterprise-scale multi-agent deployments across workflows, with shared governance and platform patterns.
Tell us where it stalled data, integration, governance, or sign-off. We’ll tell you honestly if rescue is the right move.