Since 1996
AI is only as good as the data beneath it.
Your AI gives wrong answers because it can't tell the right information from the rest of it — not because the model is weak. SureSoft builds the layer underneath: resolving, classifying, and connecting the content you already own. We've done it for enterprise search since 1996, and called it Knowledge Discovery.
- years in business
- 30+
- records migrated
- 500M+
- enterprise integrations
- 50+
- product launches delivered
- 60+
The problem
The bottleneck isn't the model. It's what reaches it.
Every enterprise AI program hits the same wall, and it isn't the model.
Search too broadly and you drown. Imagine a mining company named for the metal it mines — that name sits in the footer of every document it has ever produced. Search for the metal and you get the entire archive, which is the same as getting nothing. The word is in every file. The subject is in a few hundred.
Narrow the query and you miss instead: the part number fragmented into separate text strings inside a single drawing; the SOP that calls it a CV, because in your plant a control valve has always been a CV; or the file that names it something else entirely.
Either way, the model answers from whatever it was handed. Confidently. And wrong.
This isn't a new problem. It's an old search problem that AI inherited — and made expensive.
A bad search result used to mean a frustrated person scrolling. Now it means a confident wrong answer that someone acts on. Meanwhile the work to fix it sits in a build-team backlog you don't control — and you're accountable for a result this quarter.
What we build
The context layer
The layer between your content and your models — the one that decides whether an answer is right.
Whatever model you choose, whatever agent framework you adopt — it will find something. Whether it finds the right thing, all of it, and only what the person asking is allowed to see, is decided in the layer underneath. That's what we build.
We classify the occurrence, not the string. A company name in a footer is a company. The same word in a process specification is a material. Only one is worth retrieving. Only one gets registered.
And we work the other direction at the same time: a part number fragmented into separate strings inside a drawing, reassembled; compound identifiers expanded to every variant; the abbreviation your plant uses mapped to the part it means, so a search for either finds both; the same entity linked across systems that never agreed on a name.
Find every form of the thing. Register none of the false ones.
We don't replace your search engine. We give it something worth indexing.
Your search index and your agents read from the same place, because they need the same thing. We built this layer for search. Models are the newest thing to read from it.
An agent's answer is only as good as the passages it retrieves. Fill its context window with four hundred thousand documents that merely mention the word, and it will answer from them.
Repository consolidation
Merge the silos. Where content can't move, metadata unifies it anyway — so one repository serves search, AI, and retention.
Intelligent metadata extraction
Pull the identifiers, dates, parties, and terms buried inside documents and drawings.
Content classification
Classify by subject, retention category, and business domain. Rules and adaptive models working together.
Sensitivity classification
Mark what is confidential, restricted, or public, so your systems can enforce who sees what at the moment of retrieval.
Taxonomy generation and management
Build and maintain the vocabulary your content is organized by.
Entity recognition and linking
Connect the same person, part, or contract across systems that never agreed on a name.
Enterprise-scale processing
500M+ records, without hand-holding.
Integration-ready output
In a form your downstream systems can actually consume.
Search matches words. Context knows what they mean.
The practice
Knowledge Discovery
Finding the meaning buried in the content an organization already owns — and making it usable by the people and the systems that need it. We've done it since 1996, in service of enterprise search. Models are simply the newest thing to read from it.
Available now
Athena Prism
Enterprise Metadata Intelligence Platform
Extract, classify, and connect, at enterprise scale.
Apollo Refinery™
Content Quality & Transformation Platform
Repair, normalize, and transform content so systems can read it.
Daedalus DataMint™
Intelligent Data Generation Platform
Realistic test data without exposing production.
Coming
How we work
Predictable outcomes, without waiting on your backlog.
Implementation
We stand up the platform against your content, your taxonomy, your systems.
Managed service
We run it, so your team doesn't have to.
Embedded engineers
Our people work inside your team, on your problem, until it works.
Training
Your people learn to run what we built.
Scope, timeline, and cost agreed before we start. Three decades of doing this means we know what it takes.
Why SureSoft
Three decades of enterprise data work — and the scars to prove it.
Our clients have realized profit growth in the millions of dollars from disciplined data work, not buzzwords. We've migrated Fortune 100 content repositories, finished integrations that stalled under other vendors, and shipped more than 60 production launches for enterprises, public-sector agencies, and large non-profits.
Other vendors rarely tell you it can't be done. They tell you it can — with more time and more money. We get it done. Quickly and correctly.
You retain complete control over your information — its integrity, security, metadata, analysis, and use.
More about the companyHave a hard data problem? That's what we're here for.
Tell us about the system, the constraints, and what good looks like. We'll tell you whether and how to solve it.