Capture richer signal at the front door
Today's checkbox UX produces shallow tags. Conversation produces structured intent that sharpens every downstream surface.
A Working Session for StudentBridge, by SoluteLabs
Tailor at scale.
How AI can help StudentBridge personalize, adapt, and decide faster, without changing the bet that already works.
Aim for the hour
2-3
Working session
A practical hour, three pillars, nine ideas on the table. The intent is to leave with a short list worth piloting, picked together.
Section 01 / What we understood
A picture of StudentBridge, as we heard it.
Three observations that shape everything below.
1.1 The bet on video is already winning
Gen Z spends three hours a day on short-form video. The enrollment funnel is built on video, virtual tours, and 1:1 engagement at scale. 250+ colleges trust the result. VisiTOUR alone reports a 34% lift in tour-to-lead conversion. Everything that follows extends this bet rather than challenging it.
1.2 Three pillars from our April exchange
The three areas you put in the meeting invite frame the conversation:
P1
Attract MORE
Personalization at scale
P2
Amaze MORE
Faster content adaptation
Repurpose what you already produce
AI as editor, dubber, narrator, sequencer. Authentic footage stays real. The cameras stay; the editors gain leverage.
P3
Achieve MORE
Decision acceleration
Shorten the time between question and action
Two decisions matter. The prospect's (apply, visit, deposit) and the admissions team's (which leads to chase, what to promote, when to intervene).
1.3 Why AI extends this naturally
Three capabilities power the entire StudentBridge portfolio. Personalization. High-volume content production. Behavioral data capture at every step. These are the three things modern AI is best at. The gap between what runs today and where AI is missing on the surface is the opportunity that follows.
Section 02 / The horizon
Nine ideas. Three worth piloting first.
Nine AI use cases that fit the StudentBridge product surface. Three compose into one loop and are worth piloting first. The other six are extensions for a later wave. All sit inside the enrollment funnel, between first inquiry and deposit.
| # | Use case | Product | Pillar served | Focus today |
|---|---|---|---|---|
| 1 | AI Conversational Agent on VisiTOURReplace static interest tags with a 60-second AI conversation that mixes natural language with dynamically rendered UI. Captures structured signal at the front door so every downstream personalization gets sharper. | VisiTOUR | Personalization | Pilot |
| 2 | Persona-cut Video ViewbooksAI re-sequences existing footage into persona-specific 90-second cuts. Each visitor sees a viewbook tuned to them, no new shoots. | Video Viewbook | Content adaptation | Pilot |
| 3 | Weekly AI funnel briefingsMonday morning brief per institution. Where prospects dropped, what content worked, what to do this week. Replaces dashboard mining with action lists. | Cross-product analytics | Decision acceleration | Pilot |
| 4 | Auto-recap reels from On-Demand EventsLong recorded events into 60-second highlight cuts and program-specific recaps. Squeezes more value from content already produced. | On-Demand Events | Content adaptation | Extension |
| 5 | Multilingual auto-dubbing of viewbooksAI dubs existing English viewbooks into target languages with preserved voice. Opens international recruiting without re-shooting. | Video Viewbook | Content adaptation | Later |
| 6 | AI-generated micro landing pages per inquiryWithin 30 seconds of an inquiry, AI assembles a uniquely composed landing page from existing content blocks. Drops bounce rates on every campaign. | Amplify + Viewbook | Personalization | Later |
| 7 | Dynamic SMS and email copy in AmplifyOne campaign brief, thousands of generated variants tuned to each recipient's profile. Lifts open and reply rates measurably. | Amplify | Personalization | Later |
| 8 | Generative campus map narrationAI narrator tells a different 30-second story at each tour stop per visitor persona. No re-shoots when buildings or menus change. | VisiTOUR | Content adaptation | Later |
| 9 | Yield prediction with intervention draftsML predicts which admits are most at risk of melting and surfaces personalized outreach drafts ready for counselor review. | Amplify + analytics | Decision acceleration | Later |
A loop, not a list
Use Cases 1, 2, and 3 compose. One captures the signal. Two uses the signal to personalize content. Three uses the signal to direct human action. Three use cases, one story, one customer journey. The other six are extensions for a later wave.
Section 03 / Use Case 1
AI Conversational Agent.
A Textual and Generative UI experience that replaces VisiTOUR's static interest tags with a 60-second conversation. The agent does not just send text bubbles. It renders the right component for the moment.
3.1 The problem
Today VisiTOUR opens with tappable interest tags. A high-intent prospect into tennis and marine biology with a first-generation status and a cost concern gets the same checkbox UX as a casual browser. Most visitors leave anonymous. The depth of signal caps the depth of personalization downstream.
3.2 What it does · Textual + Generative UI
The agent mixes natural-language turns with dynamically rendered UI components, chosen by the LLM based on the conversation state. Information is presented in the format that fits the moment, not crammed into chat bubbles.
| Programs | Renders chip-selectors when narrowing majors or interests. Less typing, faster signal. |
| Financial aid | Renders a cost-snapshot card with quick options the prospect can tap. |
| Athletics | Renders program imagery and level selector (club, intramural, NCAA). |
| Tour preview | Renders an interactive map of the personalized route before launching the tour. |
| Anything unstructured | Falls back to free text. The agent reads the response and structures it. |
An LLM extracts structured fields in the background. Program interests, extracurriculars, persona (first-gen, transfer, adult, parent), concerns (cost, distance, safety), intent stage (browsing, comparing, ready). Those fields feed the existing personalization engine. Every downstream surface gets sharper for free.
3.3 Who feels it
| Student | Feels heard from second one. Visual where visual helps, text where typing is faster. |
| Parent | Separate intake track surfaces cost, safety, outcomes, aid early. |
| Admissions counselor | Inherits a structured profile instead of a tag list. Higher quality follow-up in less time. |
| StudentBridge | Every product (Viewbook, Amplify, Events) gets better inputs the moment intake ships. |
3.4 Delivery timeline and investment
A fixed-price engagement with full US-hours availability across the team. These are ballpark estimates. Discovery surfaces unknowns (integration depth, data access patterns, brand-approval workflow, scope nuances) that can revise the numbers in either direction. The fixed-price SoW produced at the end of discovery is the figure both sides sign on.
| # | Milestone | Description | Duration | Investment |
|---|---|---|---|---|
| 00 | Discovery Sprint | System and data walkthrough, user-journey mapping, risk surface, fixed-price SoW lock. | 1 wk | $3,600 |
| 01 | Agent Foundation | Build the conversational agent core, data layer, and generative UI component library. Multi-tenant from day one. | 4 wk | $23,280 |
| 02 | Generative UI + Integration | Field schema and component set defined with the StudentBridge product team. Wire into the existing personalization engine. Brand-voice gates in place. | 3 wk | $17,460 |
| 03 | Rollout & A/B | Ship across the StudentBridge customer base. A/B against the current tag UI on inbound traffic. Measure fields captured per visitor, tour-completion rate, and tour-to-lead conversion lift. | 1 wk | $5,820 |
| → | Indicative total | ±15% ballpark range · $43K - $58K. Locked to a fixed-price SoW after discovery. | 9 wk | $50,160 |
Target outcome
A measurable lift on tour-to-lead conversion on top of the 34% StudentBridge already reports, plus a structured signal feed ready to power Use Cases 2 and 3.
Section 04 / Use Case 2
Persona-cut Video Viewbooks.
Most edtech can produce content faster than they can tailor it. AI as the editor solves the tailoring half without touching the production half.
4.1 The problem
A Video Viewbook today is one experience for everyone. A first-generation 17-year-old, a transfer student returning at 28, and a parent of a high-school junior see the same content in the same order. The Creative Services team produces one master. They cannot reasonably hand-cut per persona at the scale of 250+ institutions.
4.2 What AI does
Tag every clip in an institution's library on multiple axes (speaker, topic, persona fit, emotion, length, tone). At visit time, sequence a 90-second persona cut from existing footage based on the visitor's intake signal. Brand-safe templated lower-thirds, captions, music bed. No new shooting. Real footage stays real.
4.3 Persona examples
P1
Student
First-gen
Belonging-led cut
Opens with first-gen student stories, mentorship programs, dedicated support center.
Athlete
Facilities-led cut
Opens with team training, coaches, championship moments, dorms near athletic complex.
Adult learner
Outcomes-led cut
Opens with career services, employer partners, flexible scheduling, alumni stories at 30+.
P2
Parent
Parent track
Cost, safety, outcomes same library · different 90 seconds
Opens with financial aid stories, career placement rates, campus safety services, faculty access, alumni success. The second decision-maker hears the story tuned to their concerns.
4.4 Delivery timeline and investment
A fixed-price engagement with full US-hours availability across the team. These are ballpark estimates. Discovery surfaces unknowns (integration depth, data access patterns, brand-approval workflow, scope nuances) that can revise the numbers in either direction. The fixed-price SoW produced at the end of discovery is the figure both sides sign on.
| # | Milestone | Description | Duration | Investment |
|---|---|---|---|---|
| 00 | Discovery Sprint | System and data walkthrough, user-journey mapping, risk surface, fixed-price SoW lock. | 1 wk | $3,600 |
| 01 | Clip Tagging + Library Foundation | Define the four core personas with the StudentBridge content team. Stand up the AI tagging pipeline and the source content library. | 4 wk | $23,280 |
| 02 | Sequencer + Video Assembly | Build the sequencer, the assembly pipeline, and the brand-voice approval gates in the UI. | 4 wk | $23,280 |
| 03 | Brand Templating + Rollout | Brand templates per institution. Roll persona cuts across the customer base. A/B against control viewbooks. Measure watch time and click-through to apply. | 2 wk | $11,640 |
| → | Indicative total | ±15% ballpark range · $53K - $71K. Locked to a fixed-price SoW after discovery. | 11 wk | $61,800 |
How Creative Services sits inside this
Creative Services is not replaced by AI. The team still shoots, writes, and approves. AI is the editor that lets one master become four authentic cuts. The cameras stay. The editors gain leverage.
Section 05 / Use Case 3
Weekly AI funnel briefings.
Every Monday morning, every institution gets a one-page brief on what to do this week. Generated. Reviewed. Sent.
5.1 The problem
StudentBridge captures rich engagement across all five products. Admissions and marketing teams are stretched thin and rarely have the time to mine the dashboards for actionable patterns. The platform produces signal. Teams need direction.
5.2 What AI does
An LLM layered over engagement data summarizes the week, ranks drop-off points, identifies high-performing content, flags high-intent prospects with stale contact recency, and drafts the recommended next actions. Output is one page per institution, delivered by email and surfaced inside the existing analytics view.
5.3 Example brief
Monday brief · Mock
- This week. 312 prospects engaged with VisiTOUR. 217 completed a tour. 28 reached the apply CTA.
- Where it leaked. Engineering prospects dropped 70% at the lab stop. Two clips (X, Y) were rewatched 3x.
- Recommended. Promote clip X in this week's SMS to engineering inquiries. Suggested copy below.
- Action list. Three high-intent prospects without contact in 10+ days. Aaron, Maya, Devin.
5.4 Delivery timeline and investment
A fixed-price engagement with full US-hours availability across the team. These are ballpark estimates. Discovery surfaces unknowns (integration depth, data access patterns, brand-approval workflow, scope nuances) that can revise the numbers in either direction. The fixed-price SoW produced at the end of discovery is the figure both sides sign on.
| # | Milestone | Description | Duration | Investment |
|---|---|---|---|---|
| 00 | Discovery Sprint | System and data walkthrough, user-journey mapping, risk surface, fixed-price SoW lock. | 1 wk | $3,600 |
| 01 | Data + ETL + LLM Prompting | Read-only data access to the StudentBridge analytics layer. Stand up the ETL pipeline and the LLM prompting layer that turns signals into observations. | 4 wk | $23,280 |
| 02 | Brief Template + Delivery + Admin | Brief template designed jointly with the StudentBridge product team. Delivery pipeline and counselor-side admin console. | 3 wk | $17,460 |
| 03 | Rollout + Feedback Loop | Roll the weekly brief across the customer base. Counselor feedback captured each Friday and folded back into the prompts. | 2 wk | $11,640 |
| → | Indicative total | ±15% ballpark range · $48K - $64K. Locked to a fixed-price SoW after discovery. | 10 wk | $55,980 |
Why start here
Read-only on existing engagement data. Nothing prospect-facing changes. No content production needed. No Slate write-back required initially. Low LLM cost. Fast to a working demo. A natural first use case to pilot, building the data and habits the other two depend on.
Section 06 / Role matrix
Who's on the team, and what they do.
Same composition across every use case. One PM, one Architect, two AI Fullstack Engineers, one QA. No swap-outs mid-engagement, same faces from discovery through rollout.
| Role | Responsibility | Allocation | Rate |
|---|---|---|---|
| Product Manager | Scope alignment, stakeholder cadence, sprint planning, success-metric tracking, brand-approval coordination. | 30% | $60/hr |
| Architect | Technical design, system and data integration, code review, risk surface, FERPA and security posture. | 50% | $60/hr |
| AI Fullstack Engg. (×2) | Full-stack implementation, agentic tooling, unit and integration tests, CI/CD, observability, production deployment. | 100% | $40/hr |
| QA Engineer | Test plans, regression suites, automation for critical flows, release validation, accessibility checks. | 50% | $35/hr |
Discovery week runs lighter, 30 hours each from the Architect and PM to scope the build. Build phases lock in the full composition above. The 25% efficiency gain from agentic tooling is baked into AI Fullstack Engg. hours, so the rate reflects what reaches your codebase, not what we bill for keystrokes.
Section 07 / How we'd run a pilot
Discovery first, then a bounded build.
One week of discovery, then the build. Both sides see exactly what the build should be before either commits to anything bigger.
7.1 The discovery sprint · one week
Three things happen in that week. A system and data walkthrough with your engineering and product folks. User and journey mapping with the StudentBridge product and customer-success teams to understand how customers actually use the platform. Risk surface and scope lock. FERPA posture, Slate write paths, brand-voice guardrails, LLM cost envelope. Everything that could derail the build named upfront.
By Friday you walk away with five deliverables. A detailed pilot SoW. A system map. User and persona notes. A risk register. A fixed-price quote that confirms the numbers shown in the use case slides. The cost of the sprint is folded into the build if either side proceeds.
7.2 The build itself · bounded, measurable, reversible
After discovery locks the scope, the build focuses on one use case shipped across the StudentBridge customer base. Multi-tenant from day one. Bounded scope, measurable outcome, reversible if the number does not move.
01
One use case
One of the three lead use cases above. Not bundled. One thing to ship at a time.
02
One metric to move
Tour-to-lead lift, watch-through rate, counselor action rate. Defined before the build starts. Measured at rollout.
03
Built for the customer base
Multi-tenant from day one. Deployed across StudentBridge's customer footprint, not tied to a single school. Configured per institution where the design system needs it.
7.3 Governance, said upfront
- FERPA and student data. Pilots begin with de-identified data, or with production data only with the institution's IT sign-off in writing.
- Slate integration. Additive only. No writes that risk the existing sync.
- Brand voice. Every generated asset routes through a brand-approval gate before publication.
- Cost. LLM cost lands in the cents per student, not dollars. Predictable, not surprising.
- Ownership. SoluteLabs builds and operates. StudentBridge owns the IP, the data, and the customer relationship.
Section 08 / Why SoluteLabs
Why SoluteLabs.
A small senior team. End-to-end accountability. Consulting partnership, not vendor execution.
One Architect, two AI Fullstack Engineers, one QA, with PM and designer brought in as the engagement needs them. Same team across discovery, build, and handover, no swap-outs mid-engagement. Scope is locked together with you during discovery, decisions are documented in your repository, and ownership transfers cleanly at the end. The deliverable is not just shipped code, it is a working system your team understands and can operate without us.
| US-Hours Overlap | Every engineer, the technical lead, and the project lead on this engagement work US business hours. Live standups, fortnightly demos, and Slack response within your working day. No 12-hour async lag, no "we'll catch up tomorrow." This is a non-negotiable for our team composition on this project. |
| AI Agent Engineering | Production RAG pipelines, LangGraph orchestration, hallucination guardrails. This is our primary practice, not a side capability. |
| Python & AI Ecosystem | Full Python stack: FastAPI gateway, LangGraph orchestration, LangChain RAG pipeline, LangSmith observability. We own the entire agent surface end-to-end. |
| ElevenLabs Solutions Partner | Voice pipeline delivered without integration overhead. We have shipped text and voice AI agents in production at enterprise scale. |
| React & React Native | React TypeScript web chat plus React Native iOS-first mobile. No second codebase. Android available with minimal delta. |
| Higher-Ed and B2B SaaS Depth | We work with B2B SaaS platforms that serve enterprise customers. We understand multi-tenant trust requirements, brand sensitivities, and the precision needed when your customers are the operators. |
| 12 Years · US Entity | 50+ team members. Founded 2014, US entity in Delaware. Long-running engagements with B2B SaaS and enterprise teams. |
Section 09 / Next steps
If anything here resonates, one small step.
A one-week discovery sprint. Together we pick one use case, walk the relevant StudentBridge systems, map the customer journey across your platform, and come back with a fixed-price build scope that confirms the numbers in this document.
A small, bounded commitment that lets both sides see exactly what the build should be before anyone signs up for anything bigger. When the fit is there, the discovery week rolls straight into the build, the work continues without a separate kickoff.
The ask
A one-week discovery sprint, scheduled at a time that works for your team. If that feels like the right next move, we can sketch the shape of it together.