AI agent custom builds
We engineer agents into the stack you already run.
Not a platform to configure, and not another chatbot pilot. A named agent scoped to one job, wired into your CMS, CRM and analytics, governed from the first run and monitored after handover.
What we build
Six agent services for production teams
Each one replaces a specific piece of manual handling or keeps a live agent reliable. We name the job before we name the technology.
Reporting & insight
A reporting agent reads your analytics, CRM and campaign platforms on a schedule, writes the commentary your team would have written, and flags what moved outside tolerance. Nobody exports a CSV again.
Replaces: weekly manual roll-upContent & campaign ops
A content operations agent prepares drafts, variants, metadata and localisation inside your CMS, using your templates and tone rules. A person approves before anything goes live.
Replaces: copy-paste between toolsTriage & routing
A triage agent reads inbound work, checks the CRM record, applies your categories and routes each item. Anything uncertain goes to a person instead of the wrong queue.
Replaces: shared-inbox triagePersonalisation & decisioning
A decisioning agent selects the next asset, offer or journey step using your data, eligibility rules and approved content. It works inside the personalisation platform you already license.
Replaces: static segment rulesWorkflow connectors
A workflow connector sits between your CMS, CRM, finance and analytics systems and completes the structured handling a person currently does between screens.
Replaces: manual re-entryManaged AI agents
A managed AI agent service keeps production agents monitored, secure, governed and improving. We handle model and prompt changes, compliance evidence, security patching, incidents, cost controls and scheduled optimisation while your team keeps ownership and visibility.
Replaces: reactive agent maintenanceSomething specific to your operation?
Most builds start as a job nobody wants to keep doing. Describe it and we will tell you whether an agent is the right answer.
Describe the job →How we work
Four weeks to a governed agent in production
Week 01
Scope the job
We sit with the team doing the work, map each step and define what the agent will and will not decide.
Week 02
Wire the stack
Authentication, permissions, data access and logging connect to your existing systems.
Week 03
Run in shadow
The agent runs beside the team. Decisions are compared, scored and corrected before it acts.
Week 04
Hand over
Documentation, monitoring, an escalation path and a named engineer remain in place.
Governance
An agent you can explain to your risk team
Governance is built in the same sprint as the agent, not added once someone asks. In regulated and public-facing sectors, that is usually the deciding factor.
Audit trail
Every run, input, decision and confidence score is written to a log your team can query.
Human in the loop
Defined thresholds decide what the agent does alone and what goes to a named person.
Data boundaries
Your data stays in your tenancy. Access is scoped, logged and revocable per system.
Cost control
Usage budgets and alerts are set per agent, making spend visible before it drifts.
Delivery experience
This is not a new capability with a new name on it
AI Agent Builders by GammaDX is the agent practice of GammaDX, an enterprise platform partner delivering Sitecore and Optimizely work for utilities, sport and property clients.
BI & reporting
Automated reporting and insight
Scheduled commentary and exception flagging over existing dashboards.
Internal tooling
Research and prioritisation tools
Research and prioritisation tooling used by the GammaDX team.
Customer facing
Customer-facing AI experiences
Scoped conversational and generative use cases on enterprise sites.
Governance
AI governance and cost control
Budgets, thresholds and reporting for AI usage across programs.
Latest insights
Practical guides for production teams
What is an AI agent? A practical guide for enterprise teams
Understand the difference between an AI agent and a conventional application, then assess whether a job genuinely needs one.
Read guide →AI agents vs automation vs chatbots: choosing the right approach
Choose between deterministic automation, conversational assistance and an action-taking agent based on the job.
Read guide →How to identify a high-value AI agent use case
Find repeatable work with meaningful handling cost, bounded judgement and accessible systems.
Read guide →Tell us the job. We will tell you if an agent should do it.
A 45-minute scoping call, with an engineer in the room. You leave with a written view of what an agent would do, what it connects to and what it would take to build.
What we will ask you