Tools
Your next step is a clear roadmap that helps you evaluate and adopt practical tools fast. White Star Capital found 96% of portfolio firms use horizontal tools like ChatGPT and GitHub Copilot, and 69% have built their own systems.
This guide shows how domain agents move static software into proactive systems of action. NEA highlights that agentic systems unlock access to the $11T U.S. labor spend versus about $450B in enterprise software, so early capture matters.
IBM describes domain agents as systems that embed rules, compliance, and jargon, often built on foundation models with RAG and linked via secure APIs to EHRs, CRMs, and schedulers. You’ll learn where your proprietary data becomes a durable advantage and how to protect it during development and partnerships.
Key Takeaways
- You’ll get a practical plan to evaluate and pilot industry agents quickly.
- Agent-driven systems turn workflows into systems of action that save time.
- Protecting proprietary data makes your tech a competitive edge.
- Domain approaches complement your existing software and markets.
- Real adoption stats help you set realistic goals and timelines.
Why Now: The present-day shift to industry-tailored AI
A rare convergence — cheaper models, smarter agents, and tight labor markets — makes this the moment to act.
What this means for you: 63% of companies now say intelligence is key to long-term success, and most have tried chat and copilots. Firms report rising adoption, with 69% building custom tools and 52% using them in customer service.
Your search intent decoded: what you’ll gain from this Ultimate Guide
You’ll get a plain-English analysis of where companies see traction with applications like support and ops. You’ll learn how leaders turn curiosity into measurable outcomes without a huge enterprise budget.
- Quick wins that fit your sales cycles and customer expectations.
- How to scope a pilot that limits risk and speeds time to value.
- Which parts of your workflows are ready for agents now, and which to postpone.
Bottom line: McKinsey pegs the global potential at trillions, so targeted builds or well-chosen integrations can unlock new revenue, reduce costs, and free teams to focus on higher-value work.
Defining vertical AI solutions and how they differ from horizontal AI
Putting general models to work for your field means more than plugging in a chatbox. You add terminology, compliance rules, and the exact data your teams rely on. That shift makes outputs reliable for regulated or technical tasks.
From general-purpose models to domain-specific applications
General software is broad; domain-tuned models are focused. Fine-tuning and retrieval-augmented methods lift accuracy on text-heavy work where precision and explainability matter.
Agents, workflows, and systems built for your industry
Agents act as orchestrators. They plan multi-step actions, call tools, and update records so your teams spend less time on manual handoffs.
- You’ll see how domain-focused applications beat one-size-fits-all approaches on key tasks.
- Proprietary data and supervised training drive better outcomes than generic corpora.
- Tradeoffs: faster rollouts with foundation models versus higher long-term performance from specialized training.
Use this view to map which parts of your workflows need tailored training and which can rely on foundational capabilities as you scale.
The vertical AI advantage: precision, efficiency, and defensibility
When you bind domain knowledge to your datasets, outcomes become more accurate and harder for competitors to copy.
Proprietary data, domain expertise, and feedback loops
Industry-specific records and rules let you tune models to the exact language and edge cases your teams face. That tuning improves output quality and creates a durable moat tied to your integrations and historic data.
White Star Capital highlights how nuance and deep integrations make offerings defensible. NEA adds that orchestrated agent workflows access labor budgets and raise switching costs. IBM stresses governance and human-in-the-loop loops for safety and steady improvement.
Where horizontal tools complement—not replace—vertical applications
Horizontal platforms speed experiments and lower time-to-value. But they rarely replace domain-tuned models for regulated or mission-critical tasks.
- You’ll see how proprietary data and feedback lift accuracy and efficiency while creating moats others can’t copy.
- You’ll learn when traditional saas can coexist with vertical saas to deliver quick wins without ripping out core software.
- You’ll align selection criteria—quality, usability, reliability, and price—to how your teams measure business impact over time.
For a practical pairing playbook, see the vertical SaaS and remote work guide to learn how hybrid stacks accelerate outcomes while protecting your core platforms.
Agents as the new control point: from systems of record to systems of action
Think of agents as the layer that captures intent, runs the steps, and proves the outcome before records change. This move shifts control away from legacy systems and into fast, auditable workflows you can own.
Shifting workflows: orchestration, tool use, and autonomy
Agents plan, call tools, and verify each step. They act as a platform layer that keeps context intact while the work gets done.
NEA notes agents often act before data reaches systems of record. That reduces incumbents’ switching-cost advantage and lets you capture value early.
Owning high-value tasks before data reaches legacy systems
You can place agents to own tasks that matter most. Route verified outputs to your core software, not the other way around.
“EvenUp and Klarna show outcome-based models can scale: outcome pricing and assistants handled massive chat volume while keeping human-level CSAT.”
Output-based pricing and access to labor budgets
The math matters: the U.S. labor spend is roughly $11T versus about $450B in enterprise software. That gap creates room to sell on verified work.
- Anchor workflows in systems of action to gain negotiation leverage.
- Map where agents reduce rework and route critical data to records.
- Keep humans in loop for sensitive steps while increasing autonomy safely.
Data, models, and integrations: the core stack behind vertical applications
High-quality, curated data and tight integrations are the backbone that turn general models into dependable systems you can trust in production.
Unstructured and multimodal inputs
Your stack must accept text, voice, images, video, and sensor streams. Curate and tag records so retrieval is reliable and audits are possible.
Example: transcribe call audio, attach timestamps, and link to CRM records before you run analysis.
RAG, fine‑tuning, and when to specialize
Start with retrieval-augmented generation (RAG) for fast wins. Use fine-tuning or bespoke models when inference costs or trust needs justify training spend.
NEA notes training costs are falling; some companies still choose custom models for high-value tasks.
Secure integrations into core systems
Design APIs that read and write to EHR, CRM, CAD, underwriting, and scheduling software with strict access controls.
You’ll stage data, limit credentials, and log every transaction so teams can debug outputs and meet healthcare-grade controls when required.
- Inventory structured and unstructured data for reliable retrieval.
- Decide where RAG suffices and where training pays off.
- Build secure APIs that balance performance, privacy, and auditability.
Practical playbook: how you implement vertical AI effectively
Pick a single job your team repeats daily and build a focused agent to speed it up. Start small so you can prove impact fast and iterate on quality.
Start with a wedge: choose voice agents, semantic search, or content generation to show quick wins. NEA recommends landing on one wedge and connecting it to the systems your people already use.
Integrate first, replace later
Design a land-and-expand path that improves efficiency without forcing new tools or changing workflows on day one. White Star Capital found integration is the top hurdle (56%), so prioritize compatibility and usability.
Human-in-the-loop, governance, and reliability
Set clear thresholds that route uncertain tasks to humans and add audit logs for regulated services. IBM stresses governance, explainability, and human review to keep error rates low.
- Measure impact: time saved, error reduction, and downstream revenue.
- Standardize repeatable tasks into checklists agents can follow.
- Document playbooks so your rollout doesn’t rely on one expert.
For a practical market view and integration guidance, see the vertical SaaS market guide. Use that way of planning to expand from a wedge into a durable system of record.
Industry deep dive: healthcare, life sciences, and research workflows
When medical teams use agents to transform audio and signals into record-ready text, throughput rises and errors fall.
Medical scribing, coding, and treatment summaries in EHR workflows
NEA cites Abridge as a leader that integrates with Epic to turn clinician‑patient audio into structured notes you can post directly into EHR systems. This reduces after-hours charting and keeps clinicians focused on care.
Domain-trained models help with coding and prior‑auth by retrieving guidelines and suggesting codes with higher accuracy. That reduces denials, speeds approval, and improves billing efficiency.
Biomedical research and documentation automation in pharma and devices
In research settings, agents speed literature triage, protocol drafting, and manuscript preparation. You can tune them to scientific text so summaries match the language reviewers expect.
IBM highlights multimodal platforms like Health Guardian that combine app, wearable, and sensor data for predictive models. High‑quality domain data and human review drive trust in regulated services.
“Compliant, auditable workflows are the only way regulated care scales.”
- You’ll learn how agents capture clinical conversations, generate compliant treatment summaries, and post to EHRs without adding clinician steps.
- You’ll see coding and prior‑auth improve as models retrieve medical guidelines and reduce manual lookup time.
- You’ll map a path from a one‑clinic pilot to multi‑site deployment, preserving uptime, handoffs, and governance during development.
Legal and financial services: automating core and supporting functions
You can automate repetitive legal and underwriting tasks without sacrificing compliance or client trust.
EvenUp and Harvey show how agents can take on associate-level review, extracting obligations and flagging risky clauses. Use domain-tuned models plus curated data to produce consistent, auditable text summaries for lawyers and compliance teams.
Contract review and regulatory research
Agents speed diligence by surfacing precedence, citations, and exception lists. They keep a clear trail so reviewers can verify outputs quickly.
Compliance, underwriting, and fraud detection
In finance and insurance, firms use Bloomberg‑like proprietary datasets to gain an edge. NEA notes P&C underwriters spend only ~30% on core underwriting; agents reduce admin and accelerate decisioning.
- You’ll identify tasks—contract review, diligence, regulatory research—where agents deliver auditable outputs.
- Underwriting assistants can summarize submissions, reconcile inconsistent text, and log structured decisions into your systems.
- Keep sensitive information behind secure firewalls and choose deployment patterns that meet regulator and client expectations.
- Plan routing so humans handle high-risk clauses while agents handle repeatable work to boost throughput.
Customer service, sales, and service operations you can automate today
Start small and prove impact fast: focus on repeat customer intents that waste reps’ time and let assistants take them end-to-end. When routine tickets, simple sales steps, or status requests are automated, your team spends more time on high-value work.
AI assistants that resolve tickets, summarize chats, and update CRMs
White Star Capital found 52% of companies have adoption for customer service tools. Klarna handled two-thirds of chats in one month with an assistant while keeping human-level CSAT.
Use cases: summarizing chat text, drafting replies, and posting updates to CRMs. These applications cut handle time and lift first-contact resolution.
Voice agents that capture data and trigger workflows across platforms
NEA reports voice agents land first in home services and public safety by transcribing calls, summarizing intent, and logging into CRMs and CAD systems.
IBM’s watsonx Assistant shows how to integrate assistants with backend systems to retrieve account details and initiate transactions. Design voice flows to validate identity, capture key data, and trigger the right workflow on your platform.
- You’ll identify high-volume intents agents can resolve end-to-end to cut queue time.
- You’ll map tasks—chat summaries, reply generation, CRM updates—that free reps to close sales faster.
- You’ll connect assistants so data stays fresh, tickets route properly, and reporting is accurate.
- You’ll set targets for adoption, quality, and efficiency and plan handoffs that avoid repeat questions.
Construction, public safety, and home services: real-world agent applications
Field work creates messy inputs: long spec sets, body-cam clips, and nonstop calls. You need systems that turn those inputs into clear tasks and records, fast and auditable.
Semantic document search, BIM design support, and site image analysis
Example: Trunk Tools’ TrunkText links Procore, SharePoint, and Autodesk so teams search plans and specs semantically. That reduces rework and answers field questions without slow back‑and‑forth.
You’ll see deployments where agents parse thousands of pages, flag conflicts, and suggest fixes that save time and materials.
911 voice/vision ingestion, automated logging, and report generation
Prepared began with free video/text landing for PSAPs and added voice agents that log into CAD. Axon analyzes body‑cam footage to generate reports and runs its own CAD/RMS for rapid, auditable summaries.
Why it matters: ingesting 911 audio and video in real time lets centers summarize incidents and post records with full traceability.
Inbound/outbound voice agents for scheduling and dispatch
Home services vendors (Avoca, Revin, Drillbit) integrate with platforms like ServiceTitan to handle calls, schedule techs, and run outbound sales. These agents cut hold time and boost technician utilization.
- You’ll evaluate tools your teams can adopt without retraining and prove value in days.
- You’ll assess the ability to escalate edge cases to humans in time‑sensitive markets.
- You’ll quantify impact on sales conversion, technician utilization, and customer experience as you scale across services and markets.
Build vs. buy, pricing, and ROI: how companies choose the right platform
Choosing between building and buying hinges on your data control needs, timeline, and the budgets you can commit.
When you own unique data or customers demand strict control, training your own domain model can pay off. NEA advises training when inference costs exceed training budgets, when data sensitivity matters, or when bespoke performance unlocks new markets. DeepSeek V3 is an example of a model trained at scale for under ~$6M.
When to fine-tune vs. train your own domain-specific model
Fine-tune for speed and lower cost when your application needs tweaks to general models. Train when unique datasets, strict compliance, or lifetime cost justify the upfront spend.
Tool selection criteria: output quality, usability, price, and reliability
Use a simple rubric: weight output quality (90%), usability (65%), price (38%), reliability (34%), and UI (31%) to compare vendors.
- Prioritize quality and reliability for enterprise use.
- Startups may trade some reliability for faster time-to-value.
- Factor integration complexity—56% of companies cite it as the top hurdle.
From traditional SaaS to outcome and work-based pricing
Traditional saas sells seats; work-based pricing sells verified outcomes. White Star Capital cites EvenUp’s outcome approach as a way to unlock labor budgets and speed growth.
“Klarna’s assistant proved that rapid ROI at enterprise scale can follow careful integration and outcome measurement.”
Bottom line: model ROI by deployment time, efficiency gains, and impact on sales and customer outcomes. Align procurement, security, and legal early so your business moves fast without extra risk.
Conclusion
Conclusion
Conclude with a plan that ties data, governance, and pricing to business outcomes.
You’re now set to move from exploration to execution. Focus agents where they can own workflows and deliver measurable results in your industries.
White Star Capital shows proprietary data and feedback loops create durable advantage. NEA expects agentic layers to become new control points that unlock labor budgets and shift markets. IBM stresses governance, human-in-the-loop review, and secure integrations for regulated sectors like healthcare.
Pick a wedge, pilot quickly, prove impact, and expand with a clear development plan. Choose to buy, build, or blend in a way that fits your startups or companies and the software you already use.
