Deploy production-ready agentic AI in 90 days. Sengo helps enterprise teams move from agentic AI strategy to a governed, multi-agent system on Sitecore, Optimizely, or Contentful — with Coveo, ai12z, and Law 25 compliance built in.
Start your 90-day pathAgentic AI is a class of AI system that plans and acts across multiple steps to reach a goal — instead of just answering one prompt at a time. A chatbot answers a question; an agent books the meeting, updates the CRM, drafts the follow-up email, and routes the case to the right team. For enterprises, agentic AI means software that can execute end-to-end workflows across your CMS, CRM, support stack, and data warehouse, with humans supervising rather than typing.
An enterprise agentic AI system has four moving parts: a reasoning model (the LLM), a set of tools (APIs, databases, search indexes), a memory layer (what the agent has done and learned), and an orchestrator that coordinates multiple agents. Done right, it compresses weeks of human handoffs into minutes. Done wrong, it leaks data, hallucinates customer commitments, and breaks compliance.
That gap between “demo” and “production” is where most agentic AI projects stall. Sengo’s job is to close it.
Your team built an impressive agent prototype six months ago. It still hasn’t shipped because nobody knows how to harden it for real customer traffic, or what guardrails compliance and security require.
When an agent can write to your CRM, query customer data, and trigger transactions, the old “review every output” model breaks. Your security and legal teams want a framework before they sign off — and nobody has one.
Agents are only as good as the data they reach. Your content, product catalog, and knowledge base are scattered across Sitecore, SharePoint, Confluence, and a dozen SaaS tools — without a unified search index, agents return wrong answers fast.
Every major DXP vendor — Sitecore, Optimizely, Adobe — now ships agentic AI features. Going all-in on one vendor’s stack is fast, but it locks your roadmap to their pace and their pricing. You need a vendor-neutral architecture you can evolve.
Quebec’s Law 25 already governs how you handle personal data and automated decisions. Add AI agents acting on behalf of customers, and your privacy officer needs answers most consultants can’t give. We can.
Leadership wants the AI win this quarter. Your team needs months to discover the right use cases. The 90-day path exists precisely for this — pick one production-grade use case, ship it cleanly, and use the data to fund the next.
We start with an AI Readiness Assessment tailored to your enterprise. We map agentic AI use cases against your current DXP stack, score each on business impact and technical feasibility, and pick one production-grade use case to ship in 90 days. Output: a prioritized roadmap and a ROI baseline.
Agents fail without clean data. We index your content sources into a unified retrieval layer (often Coveo or a vector store), define what each agent is allowed to read and write, and stand up an audit log so every agent action is reviewable. Law 25 controls are designed in from day one — not bolted on later.
Sengo Lab is our controlled environment for building agentic AI for enterprises. Your team and ours co-build the agents, tool integrations, and orchestration logic against real data. We test for hallucinations, prompt-injection, and unsafe tool use before any agent talks to a real customer or system.
Validated agents move into your DXP — Sitecore, Optimizely, or Contentful — with proper guardrails: rate limits, escalation paths, fallback to human review, real-time monitoring on accuracy and latency. We integrate with ai12z or other agent platforms when that fits your architecture better than a custom build.
We launch to a controlled cohort, monitor accuracy, escalations, and customer satisfaction, then iterate. Your team gets hands-on training so they own the agent in production. By day 90, you have a live agentic AI system, measurable ROI numbers, and a documented playbook to ship the next use case in half the time.
Customer support agent that resolves L1 tickets, escalates with full context, and updates the CRM
Sales-assist agent that qualifies leads, books meetings, and prepares discovery briefs
Conversational commerce agent for product discovery, comparison, and guided checkout
Internal knowledge agent that answers employee questions across SharePoint, Confluence, and your DXP
Marketing operations agent that drafts campaigns, populates Sitecore or Optimizely, and submits for approval
Content governance agent that flags out-of-policy pages, broken links, and stale articles across your CMS
Back-office data agent that reconciles records across CRM, billing, and ERP — with a full audit trail
Compliance agent that monitors content and customer interactions for Law 25 and AI Act exposure
Agentic AI proofs-of-concept that look great in a demo and never reach production
Single-vendor lock-in disguised as “the agentic platform”
Black-box agents your security team can’t audit or rate-limit
Generic “AI strategy” decks with no working software at the end
Compliance theatre — checkbox AI ethics with no enforceable guardrails
Rip-and-replace recommendations for the DXP you already invested in
Most agentic AI consultancies are platform-agnostic. We’re not. We build agentic AI for enterprises already running on a real DXP.
Each enterprise DXP exposes agentic AI differently. Sitecore’s Agentic Studio ships with native agent orchestration tied to XM Cloud. Optimizely builds agentic experiences inside its Content Marketing Platform. Contentful pairs with external agent frameworks via its content APIs. We’ve worked with all three — and we know which patterns travel between them and which don’t. Our reference architectures plug into your DXP’s content model, search, and personalization layer instead of replacing them.
An agent acts on your data. If your data is scattered, stale, or unlabeled, the agent will be confidently wrong. We start by indexing your content into a unified retrieval layer — usually Coveo for enterprise search and ML, sometimes a vector store for unstructured archives. We define source-of-truth ownership per content type, retention rules, and PII filtering. Then we wire access controls so each agent only sees what its role permits.
On the governance side, we set up an immutable audit log of every agent action: which tool was called, what data was read, what was written, and who authorized it. This is the artifact your privacy officer, security lead, and external auditor will ask for first.
One agent is an experiment. Three agents working together is a system — and that’s where most enterprise value lives. A customer support flow might involve a triage agent, a knowledge-retrieval agent, and a CRM-update agent, each specialized and limited in scope. We use orchestration patterns from frameworks like LangGraph, the OpenAI Agents SDK, and ai12z to keep agents loosely coupled, with clear hand-offs and supervisor checks. When a sub-agent goes off the rails, the supervisor catches it before the customer does.
Sengo Lab is our shared environment for building agentic AI for enterprises before it touches production. We replicate a slice of your data, stand up the agents and their tools, and run real scenarios — including adversarial ones (prompt injection, jailbreak attempts, malformed inputs). Your team works alongside ours, so by the time the agent ships, your engineers can extend and operate it without our help.
Quebec’s Law 25 requires explicit consent for automated decision-making, transparency about how personal data is used, and the right for individuals to request human review. Agentic AI systems intersect every one of these. We design Law 25 compliance into the agent architecture from day one: explicit consent capture in the user flow, decision logs the customer can request, and an escalation path to a human reviewer for any consequential outcome. We also track Canada’s AIDA developments and the EU AI Act for clients with cross-border exposure.
Agentic AI for enterprises is software that uses large language models to plan and execute multi-step workflows across enterprise systems — your CMS, CRM, support stack, and data warehouse — instead of just answering single prompts. An enterprise agent might triage a customer ticket, query the order database, draft a response, escalate complex cases to humans, and log the entire interaction for audit. The “enterprise” qualifier matters: production systems require governance, audit logs, role-based access, and integration with platforms like Sitecore, Optimizely, and Coveo that consumer agents don’t need.
A focused first use case typically takes 90 days from strategy to production. We use a five-phase path: strategy and use case selection (15 days), data readiness and governance scaffolding (15 days), build in Sengo Lab (30 days), production hardening and DXP integration (20 days), and controlled launch with measurement (10 days). Subsequent agents ship in roughly half the time because the governance, data layer, and orchestration patterns already exist.
A chatbot answers one question at a time and stops. An agent reasons about a goal, picks which tools to call, executes them, observes the result, and loops until the goal is met or it hands off to a human. Chatbots are turn-based; agents are workflow-based. For example: a chatbot can tell a customer their order status; an agent can investigate a delayed order, generate a refund or reshipment, update the CRM, send the confirmation email, and notify support — all in one session.
All major enterprise DXPs now ship agentic AI features. Sitecore offers Sitecore AI and Agentic Studio inside XM Cloud. Optimizely is rolling agentic capabilities into its Content Marketing Platform and Optimization. Adobe Experience Platform exposes agents through Brand Concierge and AEM Sites. Contentful pairs with external agent frameworks via its content APIs. The trade-off: vendor-native agents are fast to deploy but lock you into their roadmap, while vendor-neutral architectures (like the ones we build) take more setup but stay portable.
Quebec’s Law 25 requires explicit consent for automated decisions, transparency about data use, and a path for individuals to request human review. Agentic AI compliance means designing these into the agent architecture from day one, not retrofitting them later: consent capture in the user flow, immutable audit logs of every agent decision, role-based access so each agent only reads data its scope permits, and a documented escalation path to a human reviewer for any consequential outcome. We design the Law 25 controls during the data readiness phase (days 16-30), not at launch.
A first 90-day production use case typically lands between $80K and $200K CAD, depending on the data readiness work required and the depth of DXP integration. That includes the AI Readiness Assessment, governance design, build in Sengo Lab, production hardening, and team training — but not third-party platform licensing (Coveo, ai12z, LLM API costs). Subsequent use cases are usually 40-60% cheaper because the foundations carry over. Contact us for an estimate against your specific stack and use case.
Start with a free AI Readiness Assessment. We’ll map your highest-impact agentic AI use case and the path to production on your DXP — Sitecore, Optimizely, or Contentful.