Plexara
Our History
From Methodology to Plexara
Foundations: 2018 to 2022
The pieces were already in motion
Plexara would not have been possible five years earlier. The story really begins with the four primitives that quietly arrived in the late 2010s and early 2020s: a benchmark, an embedding, a database for vectors, and a paper that named the pattern everyone would soon be building.
2018
Spider sets the bar for text-to-SQL
Yale researchers publish the Spider benchmark, the first cross-domain test for natural-language-to-SQL systems. It becomes the yardstick the field will chase for the next half decade.
2019
Sentence-BERT puts meaning in a vector
Reimers and Gurevych release Sentence-BERT, making it practical to compare entire sentences by semantic similarity at scale. Embeddings move from research curiosity to production primitive.
2019 to 2021
Vector databases enter the stack
Milvus is open-sourced. Weaviate ships. Pinecone exits beta. Andrew Kane releases pgvector. Within two years, dense-vector retrieval has gone from a special-purpose technique to a feature you expect from your database.
2020
The RAG paper formalizes a pattern
Lewis et al. at Facebook AI publish "Retrieval-Augmented Generation," giving a name to the architecture that will dominate the next wave of language-model applications: retrieve relevant context, then generate against it.
2022
ChatGPT arrives
GPT-3.5 reaches a million users in five days. Every enterprise data team starts asking the same question that week: how do we point this thing at our data without giving away the keys?
The Spark: 2023
Purpose-trained models become viable
2023 is the year the toolkit arrives. Frontier models become genuinely useful for code. StarCoder and SQLCoder make local, fine-tuned generation a real engineering option. Across the industry, the dominant pattern crystallizes: retrieve known answers as context, then let a code-trained model adapt.
March 2023
GPT-4 and Claude raise the ceiling
Frontier models cross the threshold of being usefully reliable for code and reasoning tasks. Tool use and function calling land mid-year, turning chatbots into something that can actually do work.
May 2023
StarCoder ships
BigCode, ServiceNow Research, and Hugging Face release StarCoder, a fifteen-billion-parameter code model trained on permissively licensed source. For the first time, a capable code model can run inside a private network without sending tokens to a vendor.
August 2023
Defog open-sources SQLCoder
A StarCoder derivative tuned specifically for SQL generation lands on Hugging Face, posting strong numbers on the Spider variant benchmarks. Local, purpose-trained text-to-SQL becomes a viable engineering plan, not a research paper.
Late 2023
The semantic-search-plus-coder pattern matures
Across the industry, teams converge on the same recipe: encode known queries and documentation as vectors, retrieve the closest matches, hand them to a code-trained model as context, and let it draft an answer. It works, with caveats.
Origin: 2024
Methodology, an internal project at Deasil Works
Plexara begins life as Methodology, a research effort inside Deasil Works to make text-to-SQL actually work for real customer data. The thesis is simple, and at the time, slightly unfashionable: the canonical description of a business is the corpus of SQL its analysts already write.
Early 2024
A thesis: a company is its SQL
Deasil Works has spent decades running data platforms for enterprise customers. One observation keeps surfacing: the truest description of a company's data is not a wiki page or an entity-relationship diagram. It is the hundreds of ad-hoc SQL queries that analysts have written to answer real questions. Those queries encode the joins, filters, business definitions, and edge cases that nobody bothered to write down.
Q1 2024
Methodology begins as an internal project
An internal effort named Methodology starts inside Deasil Works to test the thesis. Curate the queries the analysts already trust. Describe each one in plain language. Index those descriptions as embeddings. When a new question comes in, retrieve the closest curated query, then hand it to a purpose-trained SQL coder to adapt. The pattern works well enough on customer pilots to keep going.
Mid 2024
The ceiling becomes visible
Two limits emerge at once. Methodology only knows what was seeded into it: questions that drift outside the curated set produce mediocre answers. And the purpose-trained coders, capable as they are, lack the open-ended reasoning that frontier models make look easy. Customers start asking the wrong question of the right system.
Mid 2024
DataHub joins the stack
LinkedIn open-sourced DataHub in 2020 and Acryl had been commercializing it since 2021. Methodology adds DataHub as a metadata enrichment layer over the curated queries: column lineage, ownership, glossary terms, quality scores. Answers get better. The walls of the walled garden get a bit higher, but they are still walls.
Inflection: November 2024
MCP arrives, and the strategy inverts
On November 25, 2024, Anthropic published the Model Context Protocol. For an internal project that had been steadily climbing the curated-query, semantic-search, fine-tuned-coder ladder, MCP was not an incremental improvement. It was a different game. The right move was to stop competing with frontier models and start integrating with them.
November 25, 2024
Anthropic announces the Model Context Protocol
MCP is published as an open specification with reference servers and SDKs in Python and TypeScript. The shape of the problem changes overnight. The question is no longer how to build a better text-to-SQL system. It is how to make every frontier model a first-class client of your enterprise data, with governance, memory, and semantic context preserved across the boundary.
Late 2024
Stop competing. Start integrating.
The internal decision is straightforward. Methodology had been trying to win against frontier models with a smaller, purpose-trained stack. MCP makes that the wrong fight. The work pivots: the curated query corpus, the semantic catalog, the DataHub enrichment, the governance, and the memory all become services exposed through a single MCP server. Frontier models bring the open-ended reasoning. The platform brings the context they don't have.
Q1 2025
Methodology becomes Plexara
The internal project is hardened, packaged, and rebranded as Plexara, the commercial product. Same lineage, different mission. Plexara is no longer a custom-trained answer generator. It is an integration platform that any MCP-capable client can attach to and immediately understand the business behind the data.
2025
MCP becomes the standard
OpenAI adopts MCP across ChatGPT and the desktop app. Google confirms support in Gemini. Microsoft previews MCP in Windows and across Foundry and Azure. The bet that integration would beat differentiation is settled in public. By the end of the year, Anthropic donates the protocol to the Agentic AI Foundation under the Linux Foundation, ensuring the standard outlasts any single vendor.
Today
The thesis, intact
Two years after Methodology started, the original idea has held up better than the original implementation. The truth is still in the SQL. The catalog is still where business meaning lives. The difference is what sits on top: not a smaller model trying to compete, but a governed integration layer that makes the best models in the world fluent in your business.
2026
Where Plexara stands today
Plexara is a fully managed enterprise integration platform. Federated query through Trino. A governed semantic catalog through DataHub. Persistent memory and knowledge capture as first-class services. Every component reachable through one MCP server, with fail-closed governance on every tool call. The thesis from Methodology is intact: a company's data is described by the way its people query it. Plexara is what happens when that thesis grows up, lets go of trying to be the model, and instead becomes the layer that makes every model an expert in your business.
What's next
Our Thesis
Where the history points: standards over products, composable architecture, and knowledge captured as a first-class citizen of your data estate.