Data and knowledge systems
The numbers are in the ERP, the terms are in a PDF nobody has opened since signing, and the rest lives in the heads of four people. We build the pipelines, the warehouse, and the retrieval layer that turn all of it into answers anyone can ask for in plain language, with the source attached.
What we build
Most companies need two of these, not four. We start where the pain is and add the rest when it earns its place.
The plumbing underneath everything else: your systems synced on a schedule, cleaned, modeled, and tested, so every number has one definition and one owner.
The layer that lets your team ask a question in plain language and get an answer drawn from your own documents, with the passage it came from.
Metrics defined once, in one place, so finance and operations stop arriving at meetings with two different versions of the same number.
One search box across every system, that understands what people mean rather than matching the exact words they typed.
Architecture
We adapt it to what you already run, but the five stages do not change. Each one has an owner, a test, and a way to see it break before your team does.
Everything your company already writes down, wherever it lives.
Scheduled syncs that validate on the way in and record where each row came from.
One warehouse for the numbers, one index for the text, one bucket for the originals.
Hybrid search and re-ranking, with your permissions applied before anything is read.
Wherever your team already works: a dashboard, an assistant, or an API.
Where it pays off
Every one of these started as a person walking to another person's desk because no system could answer it.
Agents answering from the current policy and the customer's actual history, instead of a wiki last edited two years ago.
Pricing, past quotes, and what was promised to a similar client, in the room, while the call is still happening.
Which agreements carry that clause, which renew this quarter, and what exactly you committed to in each one.
How much shipped, what it cost, and where it stalled, without three people exporting spreadsheets first.
New hires asking the system the questions they would otherwise interrupt a senior colleague to ask.
Runbooks, decisions, and postmortems that are findable in the moment someone needs them.
Governance
A knowledge system loses the room the first time it confidently invents something or shows a salary to the wrong person. These are not add-ons at the end.
Access is checked at retrieval, per user, against the rules your source systems already enforce. Nobody sees what they could not see before.
Personal data is detected, masked, or excluded on the way in, and we document what is stored, where, and for how long.
Every number and every passage points back to the row, the file, and the run that produced it.
Schema, freshness, volume, and business rules are checked on every run, and a failure stops the pipeline rather than quietly publishing.
We build a question set from your real queries and score changes against it, so 'it feels better' becomes a number.
Storage, compute, and model spend are measured per workload, with limits set before the bill teaches you where they should have been.
How we work
The first weeks are unglamorous on purpose. Most failed data projects were lost at the inventory stage, not the model.
01Estimate: 1–2 weeks
We list every system, file share, and spreadsheet that matters, find who owns each one, and write down what is missing or wrong today.
DeliverableA map of sources, owners, and gaps
02Estimate: 3–5 weeks
We get the data moving on a schedule, clean it, and model it so the same question asked twice returns the same answer.
DeliverableScheduled ingestion with tests
03Estimate: 3–4 weeks
We build the index and the assistant over your own content, and put it in front of the people who asked for it.
DeliverableA working assistant over your data
04Estimate: 1–2 weeks
We assemble a question set from real queries, measure accuracy and citation quality, and tune until the number holds.
DeliverableAn eval set and a quality baseline
05Estimate: Ongoing
We monitor freshness and cost, connect the next source, and hand your team the runbook for both.
DeliverableMonitoring, runbooks, and a roadmap
The timings on each stage are a reference from past projects.
Stack
We use what you already pay for wherever it holds up. When there is no constraint, this is where we start.
Your data
Everything runs in your accounts, under your contracts with your providers. We do not copy your data into our infrastructure, we do not train anything on it, and we do not keep a copy when the engagement ends.
Questions
A source inventory and a working pilot over one domain usually lands between a quarter and a half of a full platform build. We quote the inventory separately and fixed, so you can stop after it with something useful in hand and no obligation to continue.
Six to eight weeks to a system your team can query on one domain, such as contracts or support. Covering the whole company takes longer, and doing it all at once is the most common way these projects die.
Every answer carries the passage it came from, so a wrong answer is visible in one click rather than believed. We also measure it: a question set built from your real queries, scored on every change, with the number in front of you.
No, and waiting until it is clean is how companies spend three years not starting. The inventory tells us what is usable now, and cleaning happens inside the project, on the sources that turn out to matter.
Into your own cloud accounts, in the region you choose. Model providers see only what a query needs, under agreements that forbid training on it, and we can run open models inside your network when that is the requirement.
Usually not. Most of the time the warehouse is fine and the gap is everything after it: no retrieval layer, no permissions, no evaluation. We build on what is there and say so plainly when replacing something is genuinely cheaper than keeping it.
Send us one question your team asks often and no system answers well. We will come back with where that answer lives today, what it would take to make it one query, and whether it is worth building.
The first conversation is free. If your data is not ready yet, we will tell you that instead of selling you a pilot.