Why this next wave matters
Point: The first wave of AI apps focused on content generation; the next wave will restructure workflows and decision chains end‑to‑end.
Evidence: Teams get outsized ROI not from a single chat UI, but from autonomous steps that gather data, reason, act, and verify across tools.
Analysis: Treat the product as a digital coworker anchored to jobs‑to‑be‑done, not a text box. The value is closed‑loop outcomes.
Link: We’ll explore agents‑as‑a‑service, vertical models with proprietary data, and multimodal UX that makes software disappear.
Agents‑as‑a‑Service (AaaS): from chat to task completion
- Go beyond a chat interface. Design agents that own a job’s critical path with clear inputs, tools, and acceptance criteria.
- Example 1: Export‑to‑market agent for SMEs
- Input: target country, budget, SKU sheet
- Pipeline: market scan → storefront/SEO → ads setup/optimization → email/customer replies → orders/logistics
- Output: live storefront, CAC/ROAS dashboard, weekly improvements
- Example 2: Investment research agent
- Pipeline: ingest filings/reports/news → synthesize bull/bear theses → structured risk register → source‑linked report
- Guardrails: citation coverage, hallucination tests, red‑team prompts
- Design choices that matter: tool permissions, retries/timeouts, evaluation hooks, cost/latency budgets, and “stop and ask human” moments.
Vertical models + proprietary data: the real moat
- Don’t compete on generic UI—compete on hard‑to‑get signals.
- Example: Crop disease diagnosis
- Inputs: leaf images + local climate/soil metadata
- Output: disease classification + treatment recipe with dosage
- Moat: expert‑verified cases and agronomy rules fused into the model
- Example: Luxury goods authentication
- Inputs: macro photos + provenance metadata
- Moat: rare positive/negative examples and expert feedback
- Data strategy: capture “accept/edit/reject” as gold labels; build narrow RAG indexes; schedule small, frequent fine‑tunes.
- Evaluation: scenario suites with accuracy/latency/cost; run pre‑merge and nightly.
Multimodal UX: software that feels natural
- Replace dense UIs with voice, touch/gesture, and AR overlays where appropriate.
- Industrial maintenance with AR
- See machine status and repair history in‑view; ask “show last month’s vibration anomalies”; receive guided procedures.
- Architectural design workspace
- Manipulate 3D models by gesture; say “brick walls, +20% windows; recompute load.”
- Principles: fast feedback loops (<250 ms interactions), graceful degradation to 2D UI, and clear visibility of model confidence.
Pricing for uncertainty (and how to earn trust)
- Value‑based anchors: time saved, error reduction, revenue lift—not raw tokens.
- Two levers that work:
- Outcome‑linked pricing (rev‑share, qualified leads, SLAs)
- Certainty tiers: AI‑only (cheap, review needed) vs. AI + human QA (premium, quality guaranteed)
- Publish status and SLAs; show real‑time reliability to reduce perceived risk.
Build the data flywheel
- Usage → labeled signals → targeted fine‑tunes → better outcomes → more usage.
- Productize the loop: every accept/edit is a supervised signal; design prompts/UIs to gather them intentionally.
From workflow to product (turn your “AI army” into SaaS)
- Start with one high‑value workflow you already run well (e.g., content ops, claims triage, vendor sourcing).
- Generalize steps into primitives (ingest → normalize → plan → act → verify → log).
- Wrap with APIs and “agent runs” UI; add eval dashboards and cost guards.
Risks and pragmatic safeguards
- API dependency: multi‑vendor routing, health checks, and instant failover; keep a light self‑hosted model path for continuity.
- Embrace small models: distilled, quantized, and specialized models often win on speed, cost, control.
- Compliance by design: PII minimization, audit logs, regional data boundaries, and content safety filters.
30‑60‑90 day plan
- 30 days: define one agent’s job spec; ship a vertical slice with evals (quality/latency/cost); run 3 paid pilots.
- 60 days: add model routing, caching, and certainty tiers; wire outcome metrics to pricing; publish status page.
- 90 days: incorporate feedback data into fine‑tunes; expand to a second adjacent job; review per‑tenant gross margin.
Landscape: Image‑processing SaaS and tools (external links)
- Background removal and product imagery: remove.bg, Photoroom, Clipdrop
- Inpainting/cleanup: Cleanup.pictures, Adobe Firefly Generative Fill
- Enhancement/upscaling: Topaz Photo AI, Let’s Enhance, Remini
- Google capabilities: Google Photos’ Magic Eraser and Magic Editor
Internal reading (on this site)
- Prompting fundamentals — formats and reliability /posts/prompt/prompt-engineering-universal-formula-core-principles
- Advanced prompting — few‑shot, CoT, self‑critique /posts/prompt/advanced-prompt-techniques-few-shot-cot-self-critique
- DeepSeek and open strategy — implications for builders /posts/company/deepseek-ai-revolution-open-source-challenge-openai
- R1 and reinforcement learning for reasoning /posts/company/deepseek-r1-nature-cover-reinforcement-learning-reasoning
- Transformer revolution — backgrounder /posts/ai-chronicle/transformer-revolution