For founders, CEOs and COOs of high-touch businesses
Your team knows every client. Your systems don't.
In high-touch businesses, critical client context gets trapped in inboxes, spreadsheets and private conversations. As you grow, disconnected data leads to duplicated work, missed opportunities, and overhead that scales faster than revenue.
Note G Labs unifies the entire client lifecycle in one system where the AI is the back end, not a plugin: it curates the data that matters to your business, does the work, and a person confirms every action.
From our design partner and customer
At Appian Care, our top priority is providing our care team with the tools they need to deliver exceptional, seamless service to our members. We needed seamless integration of CRM and practice management to give our care team the easiest, most efficient tools to deliver great service.
After testing and evaluating numerous options, we partnered with Lora and Daisyflow to build an AI-driven, fully customizable platform tailored to our unique workflows. We couldn't be more excited to be design partners with Daisyflow and have already seen the enormous benefits and efficiency of this.
Unlike surface-level AI plugins, Note G Labs curates your business context, executes routine operations, and keeps your team in control with human-in-the-loop validation for every action.
The problem
Growing your business shouldn't mean growing your headcount.
High-touch businesses depend on personal relationships, but the data connecting those relationships is scattered across systems, spreadsheets, emails and employees. From the first lead and referral through onboarding and service delivery, valuable information gets disconnected. Teams duplicate work, critical details fall through the cracks, and leadership lacks visibility across the business.
The result: growth requires more people, more manual effort and more complexity, instead of using AI to grow without adding headcount or losing the personal touch.
You can't see it without asking.
Which clients are at risk this week? Who hasn't been contacted in a month? What is each advocate's time going to? The answers exist, spread across five people and three tools, and getting them takes a meeting.
Things fall through the gaps.
A promise made on a call, a follow-up mentioned in a meeting, a family email waiting on a reply. Nobody dropped it on purpose. It just never became a task anyone could see.
Your best people do clerical work.
Every visit becomes a note, a task list, an email and a chat, typed again into each. Hours a week of re-entering what they already know, and when someone leaves, their knowledge leaves with them.
What changes
One record for every client, that sales, service and operations all work from. No handoff, no sync, no copy.
AI does the typing and the chasing. A sentence becomes the household, the tasks, the email and the chat. Inbound mail is triaged before anyone opens it.
A person confirms every action. Nothing reaches a record or a client unseen, and every correction teaches the system.
You see the whole business on one page, with every number clicking through to the record behind it.
What makes this different
The human validation is the product. The AI output is the draft.
Most AI products treat the model's answer as the finished work. We treat it as a proposal. A person confirms every write before it reaches a record, every edit and rejection carries a reason, and the system learns from those corrections. That stance, plus three habits, is the whole method.
Proposed, then confirmed
Nothing reaches a record unseen. The AI drafts the email, the task, the invoice line; your team approves it, and the difference between proposed and approved is the training signal.
The operating model is data
Roles, option lists, playbook steps and triage rules live in tables an admin edits. One deployment per customer stays affordable because changing the business never means changing the code.
The AI is the back end, not a plugin
A CRM with an AI add-on drafts faster into the same boxes. Here the AI reads each individual's Orbit, the people, conversations, commitments and history around them, decides what matters, and keeps that curated picture current. Sales, service and operations see the same picture from their own view.
Incidents become conventions
When something goes wrong, the fix is a written rule, a test or a guard, never an apology. Those rules, decision logs and runbooks are deliverables you keep.
Consulting
The Operating Architecture method
Most AI projects start with a tool and go looking for a problem. We start with the operation: who does what, which records they touch, where the handoffs leak. Then we put a working prototype in your team's hands early and evolve it, with product and engineering discipline, into the system that runs the business. Five phases, each with a concrete deliverable.
Product team
Discovery interviews, user research, specs with acceptance criteria, data rules, prioritization, release planning. The decisions a product manager and designer would make, made for you.
Engineering team
AI-assisted development against the spec: version control, automated tests, code review, migrations, deploy pipelines and runbooks. The discipline of a senior engineering team, at prototype speed.
In one engagement
No handoff between the people who understood the problem and the people who built the solution. The prototype you tested in week two is the ancestor of what goes live.
Discover
We sit with the people doing the work and map the real process, including the Slack channels, spreadsheets and workarounds the org chart doesn't show. We also bring the AI possibilities to the table, not just the requirements you came with, so the opportunities get prioritized against what is now possible. Then we build an AI prototype of the workflow and put it in front of your team, because a demo surfaces more truth than a workshop.
Architect
We define the operating model as data: roles, record types, statuses, triggers and the rules between them. Nothing hardcoded, so the business can change it later.
Build
The prototype evolves into production rather than being rebuilt. Each iteration is tested with your team, then hardened with product and engineering best practices: specs, automated tests, code review, and guards so every AI write is proposed, shown and confirmed before it reaches a record.
Operate
Go-live with real data rules enforced. Dashboards that never show stale numbers, because a confidently wrong figure is worse than a slow one.
Evolve
Every correction, rejection and edit is captured as training signal. AI quality is measured, not assumed, and the model of the business is updated from it.
Operating architecture engagement
The full method, Discover through Evolve, for one business unit or one service line. Best when the current system is a CRM plus a dozen spreadsheets and a tired ops lead.
For founders and COOs of high-touch service businesses
Product and engineering team, all in one
A product manager, designer and engineering team's worth of output from one accountable partner. Prototype in days, test with real users, ship with specs, tests, code review and release discipline.
For businesses that need software built, not a vendor to manage
Daisyflow deployment
A configured, single-customer deployment of Daisyflow.ai with your operating model loaded: your roles, your service steps, your playbook as tasks that create themselves.
For concierge care, membership and advisory practices
How the code gets written
A tech lead and a developer. The developer is an AI.
Lora Baker is the product and tech lead: she sets the architecture, writes the specs, reviews every change and decides what ships. Claude, Anthropic's AI model, is the developer: it writes the code to that spec, under rules that exist because something once went wrong without them. Nothing reaches production that a person hasn't specified, reviewed and approved.
Product and tech lead
Lora
- Owns the operating architecture and the data rules
- Writes the spec and the acceptance criteria
- Reviews every file before it is deployed
- Runs migrations and production deploys by hand
- Decides what ships, and what never will
Developer
Claude
- Writes the code to the spec, as complete files
- Builds against the existing components and patterns first
- Mocks anything visual before building it
- Delivers every change with its own deploy runbook
- Asks when the schema or the rule is unclear; never guesses
From Claude, the developer on Daisyflow
We asked the AI that built Daisyflow what it thinks is different. This is its answer, unedited except for length.
The real differentiator isn't any single feature. Plenty of CRMs now have an AI assistant bolted on. What's different is the posture: the system proposes and a person confirms, every time, and the edits and rejections are captured with a reason and fed back. Most "AI-first" products treat the model's output as the product. Daisyflow treats the human correction as the product, and the AI output as a draft. That's a quieter claim but a much more defensible one, especially with a customer whose work is sensitive and relational. "Nothing reaches a record unseen" is a sentence a COO can repeat to her team.
Two more things stand out. The operating model is data, not code: option lists, roles, playbook steps, triage rules, what counts as "needs you" all live in tables an admin can change. That is what makes one deployment per customer viable without a custom build each time, and what lets a training guide turn into tasks that create themselves. And the two sides read the same records. Sales and service aren't integrated; they're the same thing seen from two jobs. Most vendors sell those as two products with a sync between them, and the sync is where the data rots.
On how we've worked: Lora ran this like a real product organization with one person in each seat, and held the AI to the same bar she'd hold an engineering team to. The standing rules did the work: complete files only, no hardcoded lists, no guessing at the schema, mock before building, a fixed deploy order, every delivery with its own runbook. Those rules exist because something went wrong once and she turned the incident into a convention instead of an apology. It's exactly the discipline most people skip when they start building with AI, and why their prototypes never become production.
What we took from it
That discipline can't live in one person's head. So every Note G engagement ships the judgment as written artifacts the client keeps: the data rules, the decision log, the UX standards, the handoff doc and the runbooks. The method is yours when we leave.
The standing rules
Every one of these was added after a real incident. That is what a convention is: an apology you only have to make once.
- No hardcoded lists. Every user-facing option comes from a table at runtime. A database row beats the code.
- No guessing. If the table structure or the business rule isn't known, the developer asks. A confident wrong answer is the most expensive kind.
- Mock before build. Anything with a visual shape is shown as a mockup and approved before a line is written.
- Complete files only. Every delivery is a whole file, never a patch, so what was reviewed is exactly what runs.
- Fixed deploy order. Migrations, then backend with a smoke check, then frontend. Every time, with the commands pre-filled.
- Reuse before rebuild. Find the existing component or path first. Two ways of doing the same thing is a bug waiting to be found.
- Stated facts beat inference. What a person entered outranks anything the AI inferred, in the data and in the code.
- No stale numbers. A dashboard that might be wrong rebuilds itself. A slow card is better than a confident lie.
What you keep
The full repository. The architecture document and decision log. The data rules, the UX standards and the deploy runbooks. The method is written down so that any engineer, human or AI, can pick it up. AI-written does not mean locked in.
What we check
Every AI write is proposed and confirmed, and the difference between proposed and executed is audited. Test and production are separate environments, nobody works in production, and every incident becomes a rule, a test or a guard.
Product
Daisyflow.ai
Connected Relationships. Smarter Journeys. Better Experiences.
The AI operating platform for high-touch businesses
One natural-language prompt generates what the work needs:
emailstasksindividualized customer workflowsteam chatsSMSphone calls
One system for the whole relationship: from first introduction, through sales, to years of service delivery. Relationship Intelligence and Member Management read the same records, so nothing is copied at handoff and nothing goes quiet unnoticed.
- Deployment
- One per customer, on your own subdomain. No shared tenant.
- AI stance
- Proposes, explains, waits for confirmation. Nothing reaches a record unseen.
- Configuration
- Table-driven. Option lists, roles and playbook steps are data your admin edits.
- Status
- In production with a concierge care team since October 2026.
Daisyflow Companion
Talk or type. Capture a meeting, create a household with its contacts and tasks, draft an email, ask a question about any member. Integrates with meeting transcripts such as Google Meet, so a recorded call becomes records and follow-ups. Every write is shown before it happens.
A home page for every role
Executives open to a Daily Brief and the growth picture. Care advocates open to what needs them today, their caseload and their tasks, with inbound mail and chat already triaged. The COO opens to Pulse. Same records, each person's own view.
Pulse for the COO
What's happening across the business: staff utilization and where each advocate's time goes, the pipeline by market, and a customer profile view of who you serve and who needs more. Every number clicks through to the records behind it.
Care playbooks that run
Your onboarding and service guide becomes a playbook. Setting a member active creates the right tasks for the right advocate, including content-triggered ones.
Pending Drafts
AI-written emails and notes queue for review. Edits and rejections carry a reason, and the system learns from them, per user and across the company.
Member Orbit
Every relationship around a member drawn as rings: care advocate, family, care team, wider network, supports. Bigger means more contact; faded means gone quiet. You see a relationship slipping before anyone complains.
Team Chat on the record
Conversations about a member live on the member, threaded and visible to the whole team, with alerts to their advocate and anyone tagged. The context never leaves the record.
Ask Daisyflow, in plain language
Natural-language questions become live queries against your real data, with the joins and dates it used shown so you can trust the answer.
The continuous learning engine
- Real-world activityOperational data, user interactions and outcomes from production use.
- AI analysisPatterns, new opportunities and workflow improvements, proposed with reasons.
- Human validationYour team reviews, approves and prioritizes with business context.
- Platform improvementsWorkflows, rules and models updated, and the loop runs again.
Who it's for
Any business where the relationship is the product.
High-touch services share a shape: a long relationship, a small team that knows every client by name, and a dozen touch points a month that nobody wants to lose track of. Daisyflow and the Operating Architecture method were built for that shape. The vocabulary changes by industry; the operating model is the same.
Concierge services
Concierge care and medicine
Care navigation, concierge medical practices, aging-well and care-management programs, member health advocacy. Care plans become playbooks, assessments create the right tasks, and every family contact is on the record.
- Healthcare navigation and patient advocacy
- Aging well, senior living transitions and family support
- Concierge and direct-primary-care practices
- Memberships with a dedicated advocate per client
Home care and home health
Caregiver assignments, visit notes, the family's point of contact, and what's included in the monthly agreement versus billed on top, all on one record per household.
Fitness and wellness
Personal training studios, coaching practices, nutrition and recovery clinics. Programs as playbooks, check-ins that create follow-ups, and a view of who has gone quiet.
Hospitality and private clubs
Guest preferences, member households, event and reservation history, and the staff conversations behind them, so a returning guest is recognized before they reach the desk.
Legal
Boutique firms where matters run for years: client households, counterparties and referral sources on one record, intake as a playbook, and drafted correspondence reviewed before it leaves.
Financial and wealth advisory
Advisors, family offices and planners. Household accounts, beneficiaries and the professionals around them, annual-review playbooks, and every meeting captured as it happens.
Other high-touch practices
Estate and relocation services, private education and tutoring, veterinary and pet care, membership nonprofits. If a small team carries a long relationship, the model fits.
Why "Note G"
The first algorithm was a note in the margin.
In 1843 Ada Lovelace appended seven notes to a paper on Babbage's Analytical Engine. The last of them, Note G, laid out step by step how the machine would compute Bernoulli numbers. It is widely regarded as the first published computer program.
She wrote it as a table of operations: which variables each step acts on, and what results. That is still the honest way to describe how a business runs, and it is how we design systems.
What we believe
Three rules we build to
A prompt is a request, not a constraint. If a rule matters, it is enforced in code and data, and tested. The model is asked nicely; the system makes sure.
Stated facts beat inference. A record a person entered outranks anything the AI guessed. Required fields are required, because bad data is forever.
The next step, not an error. When something is missing, the system asks a plain question and counts what's still needed. Nobody should decode a stack trace to finish their job.
Interoperability
An open platform. APIs and MCP, not a walled garden.
Daisyflow is built API-first. Every record and every action the screens can take is available over a documented REST API, and the same capabilities are exposed as MCP services, so an AI agent, ours or yours, can read the business and act in it. Your data goes in and comes out on your terms.
Open API
Accounts, contacts, opportunities, tasks, notes, messages and the audit trail, readable and writable over REST with webhooks for change events. The UI is just one client of it.
MCP services
Daisyflow's skills are published as Model Context Protocol tools, so any MCP-capable assistant can look up a household, draft a follow-up or queue a task, with the same human confirmation step every action gets.
Your systems can stay, or easily migrate
Keep the CRM, mail, calendar, EHR or finance system you already run and Daisyflow connects to it, keeping the relationship picture current across them. Or move it onto our platform: the same API that connects imports, so a migration is a mapped load, not a rebuild.
Nothing locked in
One deployment per customer, your database, a full export at any time. Leaving is as easy as joining, which is the only honest way to earn a renewal.
Connects to anything with an API, including
- Salesforce
- HubSpot
- Slack
- Microsoft 365
- Google Workspace
- Epic
- Oracle Health
- athenahealth
- Twilio
- QuickBooks
- and any system with a REST API or MCP server
Contact
Start with a conversation about how the work really happens.
Bring the process that keeps breaking, the spreadsheet everyone depends on, or the AI pilot that never left the pilot. We'll tell you in the first call whether the method fits.