Aviro

What do they actually do

Aviro builds Cortex, a runtime layer you add to existing enterprise AI agents. During long, multi‑step runs, Cortex records what the agent did, extracts reusable lessons, and surfaces those lessons mid‑run so the agent follows proven paths and stays aligned to company SOPs. The product is positioned as an add‑on to the agents, models, and tools teams already use, rather than a standalone agent or end‑user app (YC | Aviro site).

Today the company is early‑stage and selling hands‑on pilots to enterprise teams building agentic workflows. Public materials emphasize demos and partner integrations (not self‑serve signups) and describe Cortex as workflow‑aware, with reinforcement‑learning‑style improvements over time (YC | Aviro site). Aviro has shared company‑reported benchmark results (e.g., a deep‑research agent using Cortex outperforming an OpenAI baseline and ranking on Microsoft’s Deep Research benchmark); treat these as their own claims rather than independently audited metrics (YC).

Who are their target customer(s)

• Enterprise ML/AI platform or infrastructure teams building and hosting agentic workflows: Agents drift or repeat mistakes on long runs, forcing teams to maintain brittle prompt rules and intervene frequently to keep workflows on track.
• Product or research teams using agentic assistants for complex search, synthesis, or report generation: They struggle to reproduce successful runs and extract what worked, making good outcomes intermittent and costly to replicate.
• Customer support and operations teams automating ticket handling and routine processes: Automations produce inconsistent or incorrect steps mid‑workflow, driving rework, escalations, and customer friction.
• Business process owners for cross‑team automations (finance, HR, sales ops): They need predictable, auditable workflows and a way for agents to improve from past successes without constant manual tuning.
• Compliance, risk, and governance teams: They must enforce SOPs and audit behavior, but worry agents will deviate, make untraceable decisions, or lack reproducible rationale.

How would they acquire their first 10, 50, and 100 customers

• First 10: Founder‑led outreach to 8–10 enterprise teams already running long, multi‑step agents; run paid, hands‑on pilots where Aviro wires Cortex into one workflow, proves a single KPI improvement, and trades a small discount for a case study and reference calls.
• First 50: Standardize a 4–6 week pilot (integration checklist, KPI measurement, handoff playbook) and hire 1–2 sales/CS reps to run several pilots in parallel, targeting ML/AI platform teams and leads sourced from initial case studies; convert wins to annual contracts with support/SLA add‑ons.
• First 100: Productize onboarding (integration templates, SOP anchors, common connectors), add a limited self‑serve trial for low‑risk use, and work with system integrators/consultancies as channel partners; run ABM into repeatable verticals and provide compliance artifacts to speed procurement.

What is the rough total addressable market

Top-down context:

Enterprise AI spend is large and rising: IDC projects organizations will invest about $307B on AI solutions in 2025, while Gartner expects rapid adoption of agentic capabilities in enterprise apps (e.g., a jump to 40% featuring task‑specific AI agents by 2026) (IDC | Gartner).

Bottom-up calculation:

If 10,000 mid‑to‑large enterprises adopt agentic workflows that need runtime guidance and pay an average of $100,000 per year for a learning/runtime layer, the initial TAM for this niche is roughly $1.0B (10,000 × $100k). Expansion across multiple teams and workflows per account could increase this materially.

Assumptions:

• 10,000 mid‑to‑large enterprises will actively deploy agent workflows that benefit from runtime learning/guidance over the next few years.
• Average annual contract value for a governed, enterprise runtime layer is ~$100k (inclusive of support/SLA).
• Initial deployment is one primary workflow per account; multi‑workflow expansion is possible but not included in the base TAM.

Who are some of their notable competitors

• AgentOps: Agent observability and debugging for AI agents (tracing, session replay, error analysis). Notable because many teams start with observability to diagnose agent drift before adding runtime guidance.
• LangSmith (LangChain): Tracing, evaluation, and dataset management for LLM apps and agents. Widely adopted by teams building agentic workflows seeking visibility and evals.
• Langfuse: Open‑source LLM observability and analytics (traces, metrics, evaluations). Popular for teams that want self‑hosted telemetry and evals for agents.
• Humanloop: LLM development platform focused on evaluation, feedback loops, and iteration to improve model/agent performance with less manual prompt work.
• Azure AI Agent Service/Factory: Microsoft’s emerging agent services and observability guidance inside Azure AI. Relevant as enterprises standardize on cloud‑native tooling for agent development and monitoring.