DoMore.ai is a company providing forward-deployed AI engineering services, with the goal of applying AI across the entire enterprise and redesigning how work gets done. It works embedded with mid-market and enterprise teams, introducing AI and automation in coordinated waves while preserving critical systems, operational knowledge, and human judgment, thereby increasing business speed and enabling employees to adapt in step.
[Purpose and Positioning] DoMore.ai primarily addresses the "production gap": AI demos are easy, but changing how work is actually done is hard. The difficulty is not in adding an automation tool, but in understanding how work flows across teams, systems, decisions, and exceptions, and changing it without losing the knowledge, control, or technology the enterprise depends on. Its services are designed to reduce coordination (such as status tracking, duplicate data entry, context gathering, and manual routing), accelerate execution (continuous flow from intake to decision to action, while humans retain control over true exceptions), and increase capacity (letting AI handle repetitive coordination and execution so employees have more time for judgment, customers, and improvement).
[Main Capabilities and Service Process] DoMore.ai delivers company-wide transformation "one department at a time," and a typical first wave process includes:
- Week 1: On-site learning. Work alongside employees and leaders in the first department, tracing real queues, systems, handoffs, exceptions, and judgments, understanding the nuances of operations, and identifying what should not be automated.
- Weeks 2 to 4: Audit and business case. Turn field research into a decision-ready audit report, including: mapping departmental workflows and cross-functional dependencies; the boundary between AI actions and human judgment; existing systems that need to be retained, connected, or replaced; whether to build, buy a product, or use a combined path; expected ROI for each candidate workflow; requirements for permissions, risk, and employee adoption; and a prioritized first-wave implementation roadmap.
- Months 2 to 3: Implementation. Implement the highest-value workflows in the existing environment and test them on real cases. The employees responsible for the work participate in shaping controls, receive practical enablement, and help decide when the system is ready to take on broader responsibility.
- Continuous improvement: Track quality, adoption rate, speed, cost, and exceptions, improve models, tools, controls, and processes, and advance validated capabilities to the next wave of company-wide expansion.
[Technical Strategy and Tool Selection] DoMore.ai believes the right solution is usually a connected system rather than a singleTools. Its strategy includes: connecting systems that are already effective (integrating a company's existing systems, data, and workflows); adding proven AI products (when existing products can deliver the needed capabilities faster, finding, validating, and integrating them); and building the unique parts (creating custom agents and automations for a company's distinctive logic, controls, data, or operational advantages). Most implementations combine all three approaches, choosing based on fit rather than a preference for custom code. Its AI research agents continuously monitor the market, tracking new products, shifts, capabilities, integrations, pricing, and availability, and combine this with a growing catalog of evaluated AI tools to help engineers identify and validate the best option for each implementation.
[Featured Tool Catalog (Live Catalog)] The official website showcases featured AI products currently in the catalog, covering:
- Developer tools: Codex (OpenAI's software engineering agent, which can handle local repositories, cloud tasks, IDE sessions, terminal workflows, and code review; it can inspect code, edit files, run commands and tests, and delegate longer tasks in isolated cloud environments); Claude Code (Anthropic's coding agent, which can read codebases from the terminal or supported IDEs, edit files, run commands, debug, refactor, and automate development workflows; team and enterprise controls add centralized settings, permissions, and usage visibility).
- Voice generators: ElevenLabs (offers web tools and APIs for generating speech, cloning voices, transcribing audio, dubbing media, creating music and sound effects, and deploying conversational agents; the creative platform serves content production, while ElevenAgents supports voice and chat automation across customer channels).
- Personal assistants: OpenClaw (a local-first personal AI assistant that connects models, tools, and communication channels; it can work through channels such as WhatsApp, Telegram, Discord, Slack, Signal, and iMessage, with gateways and state remaining on infrastructure controlled by the user).
- App building: Lovable (turns natural language instructions into editable full-stack web apps, including frontend, backend, database, authentication, integrations, and deployment; it supports rapid prototyping and continuous product development using real code and hosted cloud services).
- Video generators: Seedance (ByteDance Seed's video generation model series; Seedance 2.0 accepts text, image, audio, and video references to generate or edit multi-shot audiovisual sequences, with control over subjects, actions, camera movement, composition, lighting, and sound). The official website states: Featured displays are alwaysIt will never change the factual status of the product, its limitations, alternatives, or the right to reject the list.
[Reference Systems and Production-Ready Design] The reference systems show the starting point of each first wave, which existing tools are retained, where humans retain judgment, and how subsequent waves connect adjacent workflows and functions. Examples include: multi-entity finance teams (closing the books without catch-up, the books-ready system monitors reconciliations, collects missing evidence, prepares exception queues, and gives controllers a real-time operational view before close day); B2B revenue teams (the agent system assembles approval packages, checks pricing and margin policies, routes decisions, updates CRM, and prepares customer handoffs); commercial, distribution, and logistics teams (resolution agents connect tickets, orders, payments, inventory, shipments, and policy context, then take approved actions and record outcomes everywhere that matters). Production-ready design emphasizes: authority is earned through evidence. Useful agents can act; production agents also know their boundaries, record their decisions, evidence, actions, and overrides, and hand judgment to the right people. Specifically: representative evaluation (testing against real cases, edge conditions, and unacceptable outcomes before expanding authority), permissioned action (each agent receives only the systems, data, and decision rights required for its role), human judgment gates (keeping a person in control when policy, risk, confidence, or materiality requires it), and visible operations (recording inputs, decisions, actions, fallbacks, and overrides so the system can be governed).
[Frequently Asked Questions (Summary of Official Website FAQ)]
- How it is delivered: design the transformation for the whole company, then deliver it in bounded, coordinated waves. Each wave typically focuses on one department or operational area, retains the parts already serving the business, and creates the foundation for the next wave.
- Whether it replaces existing tools: usually not. Retain the systems already serving the business and build an execution layer around them. Replace tools only when the evidence shows that keeping them would limit value, reliability, or adoption.
- Timeline: after the first-month audit, a clearly bounded departmental wave typically enters implementation in months 2 and 3. Company-wide transformation is a phased plan; cross-functional dependencies, integrations, permissions, data quality, risk, and adoption determine the timing of each wave.
- How to decide build versus buy: compare the existing technology stack, available AI products, integration fit, total cost of ownership, risk, and differentiation. Research agents and tool catalogs with evaluations help identify current market options before recommending a build.
- Security and adoption: the system is designed around users' environment, data boundaries, operational knowledge, and designated internal owners. Employees doing the actual work help shape and test each wave. Documentation, enablement, and adoption support are part of delivery.
[Limitations and Shortcomings] The materials do not providespecific pricing, subscription models, platform compatibility details, service area restrictions, data privacy compliance certifications, specific customer case names, or performance data. This information is not yet provided.
[Applicable Scenarios] DoMore.ai is suitable for mid-market and enterprise organizations that want to systematically introduce AI and automation company-wide but need to retain existing systems, operational knowledge, and human judgment. It is especially suitable for departments with intensive cross-team coordination, such as finance, revenue, commercial distribution, and logistics, as well as organizations that want to advance transformation in a phased, governable way.
