What would it actually take—in money, time, specialized headcount and organizational effort—to independently reproduce the capabilities already embodied in Trilivy.AI?
The answer is not hidden in a line-of-code calculation. It lives in the accumulated architecture, integration, content, testing, product decisions and operational maturity behind the code.
It is a connected product system: intelligence, ingestion, applications, agents, learning, documentation, APIs and mobile delivery surfaces operating around one platform brain.
The human-facing Bot is an orchestration environment with streaming, history and summarization. The machine-facing layer exposes the same intelligence through unified APIs, an Action Registry, integration client and MCP connectivity for outside agents and applications.
The learning engine is not simply an LMS. It creates educational assets from the underlying knowledge: lessons, objectives, assessments and persona-oriented explanations—and can feed structured Q&A and topics back into the retrieval corpus.
The effort changes dramatically depending on whether the new team receives the source and architecture, a detailed specification, or merely the concept and expected functionality.
Understand, secure, modernize, test, document and redeploy an existing body of working code. This is not an independent reproduction; the original intellectual capital is already supplied.
The destination is described in detail, but engineering still has to recreate the implementation, integrate the parts, validate quality and rebuild production maturity.
A strong product organization must rediscover architecture, product behavior and integration decisions while simultaneously implementing and testing the system.
| Workstream | Typical capability required | Modeled person-months |
|---|---|---|
| RAG / intelligence engine | AI engineers, retrieval, evaluation, embeddings, ranking, data engineering | 65–90 |
| Document ingestion & factory | Parsing, transformation, metadata, pipelines, generation, validation | 40–60 |
| LMS + AI authoring | Learning design, authoring workflows, assessments, persona generation | 45–65 |
| Bot & orchestration | Conversation state, streaming, history, summarization, UX | 30–45 |
| Agents / API / MCP / actions | Integration engineering, registries, schemas, tool execution, interoperability | 40–60 |
| Enterprise application layer | Full-stack application, admin, business workflows, security interfaces | 65–90 |
| Mobile applications | Flutter/Dart, iOS/Android testing, API integration, release engineering | 30–40 |
| DevOps / database / security | Cloud, deployment, observability, secrets, performance, resilience | 25–35 |
| QA / test / automation | Functional, regression, integration, performance and acceptance testing | 40–55 |
| Architecture / product / UX | System design, prioritization, workflow design, product decisions | 35–50 |
| Documentation / training / content | Technical writing, curriculum, labs, assessments, governance material | 45–70 |
| Indicative full-product replacement effort | ~460–660* | |
*Workstreams overlap. The table is a modeled allocation, not an additive staffing roster. Rounded headline range used in this paper: ~450–650 person-months.
AI coding tools can compress implementation time. They do not remove the need to decide what the system should do, how its parts fit together, how retrieval quality is measured, how enterprise data is governed or how the product survives production use.
That matters. A 2026 rebuild should assume modern AI-assisted engineering rather than 2022 productivity. IBM itself markets AI-enabled consulting assets to improve delivery productivity and speed to value.
The scarce resource becomes coherent architecture: data boundaries, ingestion choices, retrieval evaluation, API semantics, action models, permissions, observability, learning workflows, deployment decisions and the knowledge of what failed before the final design worked.
Large-system-integrator economics are different from those of a lean product company. A Fortune 500 program adds discovery, enterprise architecture, governance, PMO, security, UAT, change management, documentation, rollout and commercial overhead.
What an ambitious first proposal could plausibly look like before scope and commercial negotiation.
Our practical estimate for a full Fortune 500-style build with enterprise hardening and multidisciplinary delivery.
Not everyone stays for the full engagement. Teams phase in and out as architecture, engineering, QA, security, training and deployment become dominant.
This is an analytical model of a comparable Fortune 500 program. It is deliberately presented as an estimate—not as published pricing from any named consulting company.
| Enterprise work package | Modeled SI price |
|---|---|
| Discovery, enterprise architecture & requirements | $1.0–1.5M |
| RAG / ingestion / retrieval / evaluation platform | $2.5–4.0M |
| Bot + orchestration + conversational layer | $1.0–1.5M |
| Agents / API / MCP / Action Registry | $1.5–2.5M |
| Document factory | $0.8–1.5M |
| LMS + AI authoring engine | $1.5–2.5M |
| Enterprise application / interface layer | $1.5–2.5M |
| Two mobile applications | $0.7–1.2M |
| Security / governance / DevOps / observability | $1.0–2.0M |
| QA / performance / UAT / hardening | $1.0–1.5M |
| Technical documentation + learning content | $1.0–2.0M |
| Program management / change / deployment | $1.0–1.5M |
| Analytical envelope before overlap / commercial shaping | $14.5–24.7M |
The central economic idea is simple: an enterprise should not spend two years rediscovering the foundational AI platform before it can begin solving business problems. Establish the reusable intelligence layer first. Then let use cases, agents, models and applications compound on top of it.
The strongest commercial argument is not that a $75,000 Jumpstart is “worth $20 million.” It is that an organization can acquire the accumulated result of a multimillion-dollar development journey instead of paying to repeat that journey from zero.
24–36 months.
30–40 specialists at peak.
Architecture still to be discovered.
Integration and product risk still ahead.
Begin with an operating Platform-First foundation, its tooling, training, methodology, interfaces and accumulated implementation knowledge already present.
The first benefit is not cheaper coding. It is avoiding months of fundamental platform design before the organization can deliver the first serious capability.
Ingestion, retrieval, actions, APIs, MCP, Bots, learning and documentation already coexist in one architecture rather than arriving as disconnected experiments.
The platform layer should survive changes in LLM vendors. Model progress becomes an input to the platform—not a reason to rebuild the enterprise architecture.
The strategic asset is the governed knowledge and capability layer around enterprise data, not dependency on a single external conversational interface.
Third-party agents can remain excellent front ends and specialized workers. Through APIs, actions and MCP, they can consume enterprise intelligence instead of becoming isolated knowledge silos.
Open-source, LLM-agnostic and designed to avoid vendor lock-in: tooling, methodology and training support technology transfer and reverse-engineering readiness rather than black-box dependency.
A company can always hire engineers. It can always buy a model. It can always call an integrator. The expensive part is turning those ingredients into a coherent, reusable enterprise capability—and learning what architecture survives contact with reality.
The platform inventory and counts in this paper are supplied Trilivy.AI figures. Replacement cost, staffing and timeline are analytical estimates. External sources are used to anchor current labor economics and enterprise AI delivery context—not to imply that any named firm priced this project.