A client gets a link. They talk through the project. A structured scope lands in your dashboard, ready to review. No back-and-forth, no missed requirements, no lost deals.
Built & Live
The Problem
Discovery calls end. Notes sit in inboxes. A junior writes the scope from memory three days later. The client has already moved on.
"We lose 20–30% of qualified leads because our scoping process takes too long. By the time the proposal is ready, the client's already talking to someone else."
Design agency principal, 12-person firm, US
"The real cost isn't the proposal time. It's the six-figure projects that never got a chance because we couldn't respond fast enough."
Monocular user interview, April 2026
The Real Cost
Service firms don't lose deals at delivery. They lose them in the gap between interest and proposal: a gap that averages five days and eight unbillable hours.
The Product
A complete AI-powered pipeline. No workflow changes. No client training. It works the way firms already work.
Pick a project template: web, mobile, brand, e-commerce, and more. Pre-fill client details. Live in seconds.
No account, no app download. The client gets a link, opens it on any device, and has a 15-minute conversation. The AI probes for everything specific to that project type — budget, timeline, stack, risks.
Deliverables, milestones, pricing range, risk flags, assumptions, recommended tech stack, out-of-scope — enriched with live market-rate research and any reference docs or screenshots the client uploaded. All structured and editable.
Edit inline. Share a client review link. Export PDF. Send directly. Track status from draft to won.
Why Monocular
"Can't I just use a general AI tool for this?" Yes, and your team probably already tried it. Here's why the output ended up getting rewritten from scratch anyway.
| Generic AI General-purpose chat tools |
Workflow Tools Proposify · HoneyBook · Dubsado |
Monocular Purpose-built scoping engine |
|
|---|---|---|---|
| Multi-turn intake conversation (async, no scheduling) | Manual | ✗ | ✓ |
| Domain-expert AI persona (probes scope-creep, project type, integrations) | ✗ | ✗ | ✓ |
| Configured with your firm's rates, risk flags, and project templates | ✗ | Partial | ✓ |
| Structured scope output: typed deliverables, milestones, risk flags, pricing | Freeform | Manual entry | ✓ |
| Ingests client reference docs, screenshots & live market-rate data directly into the scope | ✗ | ✗ | ✓ |
| Mandatory human review gate before any client-facing output | ✗ | ✗ | ✓ |
| Full workflow: draft → review → edit → approve → PDF → send | ✗ | Post-manual | ✓ |
| Per-run trace and audit log (every AI call recorded) | ✗ | ✗ | ✓ |
| Vertical expansion via config: same engine, new domain | ✗ | ✗ | ✓ |
| Data flywheel: output calibrates to your firm's history over time | ✗ | ✗ | ✓ |
Memory is recall. Monocular is a system. An AI chat with memory can remember your rates and past scope docs. What it can't do: detect when a 15-minute conversation has collected enough to generate (that's a classification problem, not recall); enforce that a human reviews the output before it reaches a client (that's a workflow, not a prompt); isolate each firm's data from every other firm (that's multi-tenancy); or record what happened after: did pricing hold, how much did the reviewer edit, was it won? (that's the data flywheel).
You'd spend weeks stitching these together, and you'd end up with a Monocular-shaped system that still needs domain tuning, trust infrastructure, and PDF export. We built that system so firms don't have to.
One Engine · Every Vertical
A vertical = persona prompt + extraction schema + risk library. Swap the config, not the code.
CMS vs custom, auth, integrations, content migration, hosting, SEO, mobile, cookie compliance, scope-creep triggers
Matter type, jurisdiction, parties, deadlines, privilege concerns, billing structure, complexity flags
Specialty, visit type, EHR system, HIPAA posture, patient flow, referral sources, payer mix
Project type, site conditions, regulatory requirements, design intent, phasing, CAD deliverables
Infrastructure, integrations, security posture, compliance requirements, team size, migration risk
Frequently Asked
(generated, reviewer-improved) preference pairs, confirmed and dismissed risk flags, win/loss outcomes with pricing deltas. A firm 12 months in has a training set calibrated to their project types, vocabulary, rates, and outcomes. No competitor can buy that data, and it doesn't reset when a new model comes out.Where We Are
We're early, intentionally. The foundation is solid. The next 90 days determine what this becomes.
Product
Agentic engine live.
Multi-tenant SaaS with a fully agentic intake engine: orchestrator loop, Zod-typed tool registry, live market-rate research, file-attachment parsing (PDFs, DOCX, images), AI-recommended tech stack, per-run trace tables with a live internal trace viewer. Branded PDF export, client review workflow, Stripe billing. Invite-only early access.
Revenue infrastructure
Billing complete and verified.
Solo $49 · Studio $109 · Agency $219/mo. Annual available. Stripe webhooks wired, DB synced. Feature gating enforced per plan.
AI Cost
~$0.05–0.09 per scope.
Frontier models with prompt caching. Cost is variable and scales with usage; margins expand significantly at volume.
Next 30 days
Admin console + first customers.
Full billing management UI, usage enforcement, marketing website launch. Design partners being qualified now. ICP: US digital agencies 5–50 people.
Market Opportunity
We start with digital agencies: fastest sales cycle, sharpest pain, most vocal community. Then we expand the engine to every professional service vertical.
417K US digital agencies, studios, and consultants. Scoping is a weekly recurring pain at every firm.
Law firms, physician practices, architecture, accounting. Same repeatable intake problem. Larger deal sizes.
0.2% of US digital agencies at $109 avg/mo. Studio LTV ~$2,100. Agency LTV ~$4,200 at 5% monthly churn.
Why Now
This wasn't buildable two years ago. Long-context LLMs capable of structured multi-turn discovery at $0.09/scope didn't exist. They do now.
Monocular sits at the intersection of three converging trends: AI that can hold a real conversation, service businesses that are capacity-constrained, and a new generation of founders who expect to run a 20-person firm like a 5-person firm. That's the window.
Internal positioning memo, Q2 2026
Roadmap
The product ships today. The platform takes 18 months. Phase 1 has a clear proof point before Phase 2 gets funded.
Business Model
Per-seat subscription with usage limits that create natural upgrade pressure. Annual plans at 2 months free. API billing in Phase 4 adds a second, higher-margin revenue stream.
| Plan | Monthly | Annual | Seats | Scopes / mo | LTV @ 5% churn |
|---|---|---|---|---|---|
| Solo | $49 | $490 | 1 | 20 | ~$941 |
| Studio | $109 | $1,090 | 3 | 75 | ~$2,092 |
| Agency | $219 | $2,190 | Unlimited | Unlimited | ~$4,202 |
Investment
The hard part is done. The intake engine is live, billing is wired, and the first vertical is shipping. We need capital and operators to close the first 100 firms.
So service firms can focus on the things that actually matter: the clients, the craft, the relationships. Not the paperwork between interest and proposal.
The Ask
Use of Funds
Burn & Unit Economics
Series A Triggers & Exit
The Team
The full-stack product is built, billing is live, and the agentic engine is in production. We're not looking for contractors. We're looking for co-builders who see what this becomes.
Solo-built the entire Monocular stack from scratch: Next.js 16, a fully agentic AI intake engine with tool-use tracing, Supabase multi-tenant database, Clerk auth, Stripe billing with webhook sync, Inngest background jobs, PDF export, and a client review workflow. The product is live in production. Pre-team, post-build. Now focused on going from engine to market, looking for the founding team to do it with.
Open Roles
Takes full ownership of the technical architecture as Monocular scales from MVP to platform. Deep in TypeScript, distributed systems, and AI/LLM APIs. You're not building features for the founder. You're building the platform the company runs on. Co-founder equity for the right person. The codebase is clean, the architecture is intentional, and the technical decisions are documented.
Owns the data flywheel end-to-end: turning reviewer edits into preference pairs, risk flag outcomes into classifiers, win/loss data into pricing regression. The trace tables and labeled dataset are live today. You build the ML layer on top. Ideal background: DPO fine-tuning, structured extraction, RAG over firm history, or production-grade LLM evaluation. This is the role that builds the moat.
Closes design partners, converts them to paid, and builds the pipeline to the first 100 firms. Agency or studio background strongly preferred. You know the ICP from the inside, not from a persona doc. Equity plus commission on a clear ramp. The product is ready, the pricing is proven, and the ICP is sharp. We need someone who can open doors and walk through them.
Deep in legal, healthcare, architecture, or accounting. Not a full-time role yet. Advisory equity in exchange for co-building a vertical config (persona prompt, extraction schema, risk library for your domain), introducing the first 3 design partners in your network, and advising through V2 launch. This is category ownership at the ground floor of a platform that will serve your entire industry.
Get in Touch
If you write early checks into AI-native vertical SaaS, or you're a co-founder who sees what this becomes, let's talk. We move fast.