AI-first software company
DisruptX builds AI agents, LLM applications and copilots that take on the reading, checking and chasing your team does all day, with a person in charge of every decision that matters. Plus the web, mobile and cloud platforms they run inside.
Task
Match 42 supplier invoices to open purchase orders
What we build
Agents and LLM applications are the centre of what we do. The product, mobile, cloud and testing work that carries them to real users is handled by the same team, so nothing gets lost between vendors.
How our AI works
Every system we ship follows the same shape: grounded in your data, acting through tools you control, checked before it acts, and watched once it's live.
If a check fails, the agent retries with more context or stops and asks. It never guesses its way past a guardrail.
Answers come from your data, with citations. When the data doesn't say, the system says so.
An eval set scores every prompt, model or code change before it ships.
Confidence thresholds and approval steps put a person on the decisions that matter.
Every step traced, with latency, quality and cost per request on one dashboard.
Recent builds, in motion
Illustrative product renders. Replace with recordings of your own shipped work.
01 / 04 · AI agent
It watches every route, reassigns stops when a driver runs late, messages customers, and sends anything unusual to a dispatcher.
02 / 04 · LLM application
Clauses extracted against a playbook, each linked to its page, with uncertain ones routed to an analyst.
03 / 04 · Mobile copilot
Ask whether you can afford a trip and get an answer grounded in your goals and spending, not a generic tip.
04 / 04 · Release gates
Unit tests, a 400-case eval set, safety checks and a cost check run on each change, then a canary watches live quality.
Replace these four figures with your own before launch.
Where AI earns its keep
Different sectors, same pattern: skilled people spending their day reading, copying, checking and chasing. That's the work an agent should take first.
Capabilities
We choose models on eval results and tools on what your team can maintain. These are the ones we reach for most.
The right model for each step, chosen on your eval results rather than on hype.
Why teams pick us
We agree what a good answer looks like and turn it into a scored test set before the build starts. Quality becomes a number you can watch, not a feeling after a demo.
Agents act inside clear limits and hand anything risky or uncertain to a person. Every action is logged, so nothing happens that you can't explain.
Caching, model routing and budgets are part of the design, so your finance team sees a predictable line, not a surprise invoice from a model provider.
The people you meet do the work. Code, prompts, eval sets and infrastructure live in your accounts from day one.
Forty-five minutes on the workflow, the data, the constraints and the number that would prove it worked. We ask what a wrong answer would cost, because that decides where a person stays in the loop.
You get: a written summary, an honest read on whether AI is the right tool for this at all, and a referral if we aren't the right team.
We collect real examples and agree what a good result looks like, which becomes the eval set. Then we choose models, map the tools and data the system needs, and test the riskiest part in a spike.
You get: an eval set, a technical design, a cost-per-request estimate, a milestone plan with a fixed price or capped range, and a working prototype of the core flow.
Design, build, test, demo, deploy, on a fixed rhythm. Every change is scored against the eval set, so you see accuracy, latency and cost move sprint by sprint, not just features.
You get: a shared channel, repository access from day one, an eval report each sprint and a Friday demo on real data you can invite anyone to.
A rehearsed go-live with guardrails, approval steps, tracing and cost alerts switched on. We roll out to a slice of users first and watch live quality before widening it.
You get: runbooks, architecture documentation, a prompt and model change process, and a recorded walkthrough for whoever joins your team later.
Models change and so does your data. Either we stay on to monitor quality, rerun evals when providers update models and tune cost each month, or we hand the system to your team with a paired transition, not a zip file and good luck.
Selected work
A clinic group ran scheduling and records on an ageing on-premise application, and clinicians spent the first minutes of every visit reading long intake forms.
We moved the system to a managed cloud environment, rebuilt the patient portal, and added an assistant that turns each intake form into a short summary for the clinician to check. Summaries never leave the clinic's own cloud account, and every one links back to the answers it came from.
Shoppers searched the way they talk, like "waterproof jacket for hiking under 80", and the old keyword search returned nothing useful. Peak traffic also took the site down twice in one quarter.
We replatformed onto a headless stack with a cached storefront, then added search that turns a plain-language request into filters and ranked results, explaining why each product matched. Load tests at peak profiles now run before every campaign.
The people
Team photo
Add a real photo of your team at work here. It does more for trust than any stock image or illustration.
Paste a two-sentence quote from a client here. The strongest ones name the problem you solved and the outcome, not just that you were nice to work with.
Teams we build for
Next step
Tell us the workflow that eats the most time. We'll tell you honestly whether AI is the right tool, what it would cost to build and run, and how long it would take.