{"@":{"v":"gdom/1.2","profile":"https://dng.ai/gibberdom/gdom-1.2.json","proj":"text","index":"https://dng.ai/_gdom/manifest.json","site":"https://dng.ai/_gdom/site.gdom","self":"https://dng.ai/_gdom/services/index.text.gdom"},"text":"# From first workflowto scaled operation.\n\nDraft & Goal pairs agentic AI engineering with enterprise marketing expertise. We scope the work, connect your stack, encode brand and governance rules, launch with your team, and stay close after go-live.\n\nDelivered with you. Never dropped off.\n\n- Time to value First workflow targeted in 30 days\n\n- Delivery model Named AI + marketing team\n\n- Ownership Your workflows, patterns, and IP\n\n## Launch. Extend. Operate.\n\nStart with the engagement that matches where you are today. Every mode is designed to leave your team with a working system and more internal capability than it started with.\n\n### Enterprise AI onboarding\n\nMove your first governed marketing workflow into production.\n\nWe select the right first use case, configure your knowledge and integrations, set acceptance criteria, and launch alongside the people who will own it.\n\nTeams moving from prompts, pilots, or backlog to a production operating workflow.\n\n#### Prioritize\n\nA workflow and ROI map grounded in business value, feasibility, and adoption.\n\n#### Configure\n\nBrand knowledge, data sources, models, integrations, roles, and approval gates.\n\n#### Launch and enable\n\nControlled production runs, acceptance testing, owner training, and a working runbook.\n\n### Implementation & integration engineering\n\nBuild the workflows and connections your operating model needs.\n\nA forward-deployed engineer works with marketing, data, IT, security, and legal to extend the platform inside your real environment.\n\nTeams with custom systems, migrations, complex governance, or a roadmap of additional workflows.\n\n#### Production workflows\n\nReusable multi-agent workflows designed around the way your teams actually work.\n\n#### Connectors and MCP\n\nCustom integrations, tools, and knowledge access built, tested, and documented.\n\n#### Migration and governance\n\nLegacy automations rebuilt with permissions, human review, auditability, and monitoring.\n\n### Managed success & optimization\n\nKeep quality, cost, adoption, and the roadmap moving after launch.\n\nYour service layer scales with the plan—from standard support to a named success team, a shared channel, review cadence, and enterprise SLA.\n\nTeams treating AI workflows as a production capability, not a one-off implementation project.\n\n#### Workflow health\n\nQuality, failures, usage, and costs reviewed against the definition of done.\n\n#### Named context\n\nPeople who know your workflows, stakeholders, constraints, and next priorities.\n\n#### Scale roadmap\n\nNew use cases prioritized and proven patterns expanded across teams and markets.\n\n## AI engineering meets enterprise marketing operations.\n\nThe hard part is not generating an answer. It is turning a marketing process—with its systems, standards, approvals, economics, and exceptions—into a reliable operating workflow.\n\n### Production-grade agentic engineering\n\nWe design for reliability, evaluation, permissions, observability, cost, and change—not just a compelling demo.\n\n- Multi-agent architecture and orchestration\n\n- Model selection, routing, and evaluations\n\n- Knowledge, retrieval, and data access\n\n- Connectors, APIs, webhooks, and MCP\n\n- Human review, audit trails, and monitoring\n\n### Deep enterprise marketing context\n\nWe understand the workflows before we automate them, from the operating metric to the final approval and publishing step.\n\n- SEO and content supply chains\n\n- Campaign and demand operations\n\n- Localization and multi-market governance\n\n- Brand systems and approval chains\n\n- CRM, CMS, analytics, and reporting\n\nOne delivery team can hold both the technical architecture and the operating reality—so less gets lost between strategy, IT, and production.\n\n### Nabil Tayeb\n\n10+ years in performance marketing and e-commerce · NextAI ’23 · Creative Destruction Lab ’24\n\n### Vincent Terrasi\n\nProduct, data, and AI leadership at OnCrawl, OVH, and Groupe M6 · 70,000+ learners trained\n\n## A concrete path to production—not a prolonged pilot.\n\nThe target is a governed workflow running on real inputs, with clear owners and acceptance criteria. Timing depends on access to data, systems, and enterprise review teams.\n\n- 01 Days 1–5 Discover and prioritize Map the process, baseline the economics, identify risks, and select the first workflow by value and feasibility. Opportunity mapROI baselineDefinition of done\n\n### Discover and prioritize\n\nMap the process, baseline the economics, identify risks, and select the first workflow by value and feasibility.\n\n- Opportunity map\n\n- Definition of done\n\n- 02 Week 2 Design the operating system Design the workflow, data access, brand knowledge, model policy, integrations, permissions, and approval path. Solution designIntegration inventoryGovernance matrix\n\n### Design the operating system\n\nDesign the workflow, data access, brand knowledge, model policy, integrations, permissions, and approval path.\n\n- Solution design\n\n- Integration inventory\n\n- Governance matrix\n\n- 03 Week 3 Build and validate Build the production workflow, test representative cases, tune quality, and validate human checkpoints with owners. Working workflowAcceptance testsReview queue\n\n### Build and validate\n\nBuild the production workflow, test representative cases, tune quality, and validate human checkpoints with owners.\n\n- Working workflow\n\n- Acceptance tests\n\n- 04 Week 4 Launch and hand over Run controlled production, monitor the first executions, train owners, and document the operating cadence. Production launchOwner runbookOptimization backlog\n\n### Launch and hand over\n\nRun controlled production, monitor the first executions, train owners, and document the operating cadence.\n\n- Production launch\n\n- Optimization backlog\n\n### Improve what production teaches us.\n\nQuality, usage, exceptions, model cost, and business results feed the next iteration and the roadmap for additional workflows.\n\n## A named team that stays close to the work.\n\nSupport is most useful when it has context. Higher service levels keep the people who know your environment connected to workflow health, adoption, and the next business priority.\n\nGrowth includes tailored onboarding and standard support. Agency adds guided onboarding and dedicated Slack. Enterprise adds a named success team, governance design, and an SLA.\n\n### Context-rich support\n\nIssues reach people who understand the workflow, its owners, and the systems around it.\n\n### Quality and cost optimization\n\nReview failures, output quality, usage, model routing, and spend against operating targets.\n\n### Executive roadmap\n\nAssess business value, adoption, and the next workflows worth launching or expanding.\n\n### Named team and SLA\n\nDefined ownership, escalation path, response-time commitments, and governance support.\n\n## The workflow changes. The numbers follow.\n\nThese outcomes come from combining production software with the process design, data, approvals, and operating ownership around it.\n\nThousands of SEO pages refreshed manually at four hours each became a governed content-refresh operation.\n\n> “You can't imagine how much time I save by using Draft & Goal. The information is ready as soon as I open the dashboard.” François Rommel · TotalEnergies\n\n“You can't imagine how much time I save by using Draft & Goal. The information is ready as soon as I open the dashboard.”\n\nA marketplace content challenge became a repeatable programmatic workflow connected to real location and vehicle data.\n\n> “AI automation has enabled us to generate thousands of unique pages in record time, while boosting our SEO and considerably reducing costs.” Julien Deneuville · Head of SEO, Turo\n\n“AI automation has enabled us to generate thousands of unique pages in record time, while boosting our SEO and considerably reducing costs.”\n\n## Know what support surrounds the platform.\n\nEvery paid plan includes onboarding tailored to its scope. Higher tiers add a closer working channel, named context, governance design, and contractual response commitments.\n\n| Service capability | Growth | Agency | Enterprise |\n| --- | --- | --- | --- |\n| Production onboarding | Tailored setup | Guided | White-glove |\n| Primary support | Email, docs, community | Dedicated Slack | Named success team |\n| Governance design | Scoped | Scoped | Included |\n| Forward-deployed engineering | — | — | Custom scoped |\n| Service-level agreement | — | — | Included |\n| Roadmap reviews | As needed | As needed | Quarterly |\n\nFinal services, capacity, response times, and commercial terms are confirmed in your order form.\n\n## The questions enterprise teams ask before launch.\n\nThe target is a first workflow in production within 30 days. The exact timeline depends on workflow scope, data and system access, and the timing of security, legal, and stakeholder reviews.\n\nMost launches need a marketing process owner, a subject-matter expert, and the people who control relevant data, systems, security, or legal review. We keep their involvement focused around decisions, access, acceptance, and ownership.\n\nAn AI engineer who works directly with your marketing and technical teams to build production workflows, integrations, and governance inside your real environment—then documents and hands over what was built.\n\nYou do. Workflows, connectors, brand and knowledge configuration, documentation, and operating patterns built for your deployment stay in your workspace, subject to the terms in your order form.\n\nThe most common dependencies are unavailable APIs, delayed data access, custom security requirements, and stakeholder or procurement reviews. We identify these during discovery and make the critical path visible.\n\nEvery production plan includes onboarding tailored to its scope. Agency adds guided onboarding and dedicated Slack support. Enterprise adds a named success team, governance design, and an SLA; forward-deployed engineering is scoped separately.\n\nYes. We can deploy alongside internal teams, agencies, or systems integrators, with clear ownership for workflow design, data, approvals, implementation, and ongoing operation.\n\nBring the workflow.Leave with a path to production.\n\nIn 30 minutes, we'll identify the first workflow, map its systems and approvals, and outline a realistic route to production.\n\n## Outbound links\n\n- [Map my first workflow →](https://dng.ai/book-demo/)\n\n- [Explore onboarding↗](https://dng.ai/services/enterprise-ai-onboarding/)\n\n- [Meet the engineering model↗](https://dng.ai/services/forward-deployed-engineering/)\n\n- [Design my service plan→](https://dng.ai/contact-sales/)\n\n- [Read the case study↗](https://dng.ai/customers/case-studies/totalenergies/)\n\n- [Read the case study↗](https://dng.ai/customers/case-studies/turo/)\n\n- [See plans and capacity→](https://dng.ai/pricing/)\n\n- [Tour the platform](https://dng.ai/platform/)\n\n- [Draft & Goal](https://dng.ai/)\n\nSite navigation: https://dng.ai/_gdom/site.gdom\n\nFull page index: https://dng.ai/_gdom/manifest.json","links":[{"rel":"internal","href":"https://dng.ai/book-demo/","path":"/book-demo","label":"Map my first workflow →","gdom":"/_gdom/book-demo/index.text.gdom"},{"rel":"internal","href":"https://dng.ai/services/enterprise-ai-onboarding/","path":"/services/enterprise-ai-onboarding","label":"Explore onboarding↗","gdom":"/_gdom/services/enterprise-ai-onboarding/index.text.gdom"},{"rel":"internal","href":"https://dng.ai/services/forward-deployed-engineering/","path":"/services/forward-deployed-engineering","label":"Meet the engineering model↗","gdom":"/_gdom/services/forward-deployed-engineering/index.text.gdom"},{"rel":"internal","href":"https://dng.ai/contact-sales/","path":"/contact-sales","label":"Design my service plan→","gdom":"/_gdom/contact-sales/index.text.gdom"},{"rel":"internal","href":"https://dng.ai/customers/case-studies/totalenergies/","path":"/customers/case-studies/totalenergies","label":"Read the case study↗","gdom":"/_gdom/customers/case-studies/totalenergies/index.text.gdom"},{"rel":"internal","href":"https://dng.ai/customers/case-studies/turo/","path":"/customers/case-studies/turo","label":"Read the case study↗","gdom":"/_gdom/customers/case-studies/turo/index.text.gdom"},{"rel":"internal","href":"https://dng.ai/pricing/","path":"/pricing","label":"See plans and capacity→","gdom":"/_gdom/pricing/index.text.gdom"},{"rel":"internal","href":"https://dng.ai/platform/","path":"/platform","label":"Tour the platform","gdom":"/_gdom/platform/index.text.gdom"},{"rel":"internal","href":"https://dng.ai/","path":"/","label":"Draft & Goal","gdom":"/_gdom/index.text.gdom"}]}