Modiqo Raises $3M to End AI's 'Sandcastle' Era of Unreliable Workflows

SAN FRANCISCO, CA – May 28, 2026 – AI infrastructure startup Modiqo has secured $3 million in pre-seed funding to tackle one of the most persistent and costly problems in enterprise technology: the unreliability of AI agents in production. The round was co-led by developer-focused venture firm Heavybit and Seligman Ventures, with participation from Irregular Expressions and angel investors.

The funding coincides with the launch of Modiqo's flagship product, Rote, a local execution layer designed to move AI from the realm of impressive but fragile experiments into the world of dependable, industrial-grade systems. The company aims to solve the paradox that has stumped countless engineering teams: getting an AI agent to work once is easy, but getting it to work consistently is a monumental challenge.

"AI workflows today are like sandcastles. They look impressive when you build them, then wash away the moment anything changes," said Chetan Conikee, Founder and CEO of Modiqo, in a statement. "Companies are paying their best people to rebuild the same things again and again. Rote captures what worked the first time and locks it in, so teams can move forward instead of starting over."

The 'Sandcastle' Problem Plaguing Enterprise AI

Conikee's "sandcastle" analogy resonates deeply within an industry grappling with the operational realities of AI. While generative AI and agentic systems demonstrate remarkable capabilities in controlled demos, deploying them into the dynamic environment of a real business often reveals their fragility. Workflows that function perfectly one day can mysteriously break the next due to a minor update in a large language model (LLM), a change in an external API, or subtle shifts in input data.

This brittleness creates a cycle of constant maintenance, what investors call a "rediscovery tax," where highly skilled engineers spend their time fixing broken processes rather than creating new value. The problem is compounded by soaring operational costs. Each time an agent runs, it consumes tokens and compute resources to reason its way to a solution. When workflows are not repeatable, companies pay for this reasoning process over and over again, causing costs to spiral from a manageable prototype budget of hundreds of dollars a month to tens of thousands in production.

Industry data validates the severity of this challenge. Experts note that even advanced models can have success rates as low as 35% on complex tasks, and general reliability in real-world deployments often hovers around 80%—a figure far too low for mission-critical business applications in finance, healthcare, or logistics. This reliability gap is a primary reason why so many AI pilots fail to graduate to production. Market analysis from firms like Gartner has projected that a significant portion of agentic AI projects could be canceled in the coming years due to escalating costs, unclear value, and a lack of risk control. Another industry report found that while AI's vision is grand, only a sliver—around 11%—of agentic use cases actually made it into production last year, with a majority of business leaders citing a major gap between promise and delivered value.

From Chat Log to Deterministic Code: How Rote Works

Modiqo is positioning Rote as the foundational layer to solve these issues. Instead of focusing on prompt engineering or model fine-tuning, Rote operates at the execution layer—the point where the AI agent interacts with other software and systems. The tool acts as an observer, watching an agent as it successfully completes a task. It then analyzes that successful execution path—the specific sequence of tool calls, API interactions, and data manipulations—and turns it into a deterministic, reusable piece of code.

This captured workflow can then be executed repeatedly with high fidelity, bypassing the need for the AI to reason from scratch every time. The benefits are threefold:

1. Reliability: By turning a successful, non-deterministic process into a fixed, deterministic one, Rote ensures the workflow performs the same way every time, insulating it from the volatility of model updates and API changes.
2. Cost Reduction: Reusing a proven workflow dramatically cuts down on token consumption and expensive inference calls. The AI's reasoning power is reserved for novel problems, not for re-solving tasks that have already been figured out.
3. Observability: The platform provides a clear, auditable trail of what ran, when it ran, and what it cost, bringing much-needed transparency to often opaque AI operations.

"Every serious team running agents is hitting the same wall: reliability, cost, and reproducibility," noted Jean Sini, founding partner at participating investor Irregular Expressions. "Modiqo is the first team I've seen attack all three at the execution layer rather than the prompt layer."

Investor Confidence in Foundational AI Infrastructure

The $3 million investment signals strong investor confidence that the next major wave of AI innovation will be in building the foundational infrastructure required to make it work at scale. The market for such tools is expanding rapidly, with the overall AI infrastructure market valued at over $135 billion in 2024 and projected to more than double within the next six years. The more specific MLOps market, focused on managing the machine learning lifecycle, is also experiencing explosive growth, expected to surge from a few billion dollars today to over $30 billion by the early 2030s.

Heavybit, a co-lead investor, has a long track record of backing developer-first and infrastructure-focused companies. Their investment underscores the thesis that AI needs its own set of robust, developer-centric tools to mature.

"Today's AI agents are impressive in the demo and unreliable in production," said Joseph Ruscio, General Partner at Heavybit. "The deeper problem isn't agent capability — it's that every agent run is effectively a one-off chat log rather than an artifact that can be proven and iterated on. Modiqo's Rote captures what worked the first time and turns it into deterministic code... building the execution layer agents need to graduate from experiment to production infrastructure."

Co-lead investor Seligman Ventures echoed this sentiment. "The industry is quickly realizing that building capable AI agents is only part of the challenge, operating them reliably at scale is the harder problem," stated Umesh Padval, Managing Partner at the firm. "Modiqo is building a critical foundational AI infrastructure layer that enables teams to deploy AI workflows reliably at a fraction of the token cost."

Navigating the Competitive Landscape of AI Orchestration

Modiqo enters an increasingly active but still formative market. A growing ecosystem of tools is emerging to help manage AI agents, from open-source orchestration frameworks like LangGraph and CrewAI to fully managed platforms from major cloud providers like AWS Bedrock Agents and Google Vertex AI. These platforms excel at coordinating the flow of tasks between different agents and managing their communication.

However, Modiqo is carving out a distinct niche by focusing on the fundamental reliability and repeatability of each individual action within a workflow. While orchestration platforms manage the 'what' and 'when' of agent tasks, Rote is concerned with the 'how'—ensuring that once a successful 'how' is found, it becomes a permanent, efficient asset for the organization. This focus on the execution layer, rather than just the orchestration or prompt layer, differentiates its approach from many existing solutions that still rely on probabilistic AI reasoning for every step.

By aiming to make AI workflows as reliable and reusable as traditional software functions, Modiqo is addressing a core pain point that has slowed enterprise adoption. The company's success will depend on its ability to convince engineering teams that the solution to AI's unpredictability lies not in more complex prompting, but in building a more stable foundation underneath. By focusing on turning singular successes into institutional knowledge, Modiqo is betting that the key to scaling AI isn't just making agents smarter, but making their execution predictable, repeatable, and fundamentally reliable.