Fewer mistakes
Software does not get tired. When it reads a form, checks a database, or moves a file, the data stays clean.
AI and ML for the bottom line
Practical AI & ML for real business outcomes
We build tailored AI & ML solutions that automate repetitive work and turn complex data into usable information, helping your team save time, reduce errors, and make better decisions.
Scanned PDFSearchable text
Recorded callNotes you can find
New leadReplied in seconds
Same work. Nobody had to sit through it.
Our AI/ML expertise
The tools we use to buy back your time
Core focus
- AI strategy consulting
- Generative AI
- Machine learning
- OCR (text recognition)
- ASR (speech recognition)
- Agentic systems
Other things we do
- Business intelligence
- Data engineering
- Platform development
Our stack
- Cloud & compute
- AWSGCPVertex AIBedrock
- Models
- OpenAIAnthropic (Claude)GeminiHugging Face
- Frameworks & agents
- PyTorchLangChainLangGraphCrewAI
- Vision & voice
- WhisperPaddleOCRTesseract
- Testing & quality
- LangSmithstandard evals
What we do
We work like a doctor
You tell us what hurts. We check what is really happening before naming the treatment.
Listen
We start with the problem, not the tool. We ask where hours go, where errors appear, and what costs money.
Examine
We look at the way the task gets done today. We inspect inputs, handoffs, edge cases, and the final decision. Then we mark where a model could help.
Prescribe
We pick the simplest thing that can work. Sometimes that is an older open-source model. OCR is the clearest case. There, older models are often the better answer.
Observe
We stay close after it goes live. We watch real inputs hit the system. If it misses cases or needs adjustment, we fix it.
Support
A model cannot work alone. We build the pieces around it, from data labeling tools to system connections, to make sure the treatment takes.
What it means for the business
The work should show up in the business
If it does not save time or money, it is probably not worth doing.
Hours saved
Meetings, calls, documents, and images can become text people can search. Staff can review the important parts instead of every file.
Boring work gone
Software can copy fields, check a record, send a draft, or file a note. People stay in charge of the parts that need human judgment.
Faster lead contact
When a new lead arrives, software can kick off first steps within seconds, keeping the lead warm.
Case files
What the treatment looked like
We build on standard tech and keep our clients’ data and proprietary models strictly confidential. Here is what we can share, and we will walk through the concrete numbers and architecture on our first call.
Marketing agency
Scaling influencer screening for a marketing agency
- 80%
- less screening time
- 20%
- of projected LLM cost
The agency reduced screening time by 80% and processed content at 20% of the estimated cost of a general-purpose LLM workflow. The system gave the team a more efficient way to assess potential brand ambassadors and focus its attention on flagged content.
Read the full chart
Context
A digital marketing agency needed to screen thousands of influencer videos before recommending creators for brand partnerships and user-generated content campaigns. The process required reviewing both spoken content and text appearing within each video.
Pain point
Extracting information was only the first step. Transcripts and on-screen text needed to be organized, classified, and flagged against screening criteria. Handling this volume required significant processing time and data infrastructure, while missed issues could expose clients to unsuitable brand partnerships.
What we did
We developed a custom video-screening system that combined audio transcription, frame-by-frame text recognition, and automated content classification. We built the supporting data architecture and post-processing tools to handle large volumes and turn raw content into organized findings for the agency’s review team.
Engineering
Turning legacy engineering drawings into usable AutoCAD data
- 3
- employees freed up
- $400k
- annual value recovered
The solution helped the company complete the project on schedule while freeing three employees for higher-value work. The recovered staff capacity represented an estimated annual value of approximately $400,000.
Read the full chart
Context
An engineering company needed to recover parts lists and technical information from legacy drawings. These documents had to be scanned, interpreted, and converted into structured data that could feed into its AutoCAD workflow.
Pain point
The company relied on four employees to extract and prepare information manually. The work consumed substantial engineering capacity, carried a high labour cost, and kept skilled staff occupied with repetitive document processing.
What we did
We built an end-to-end document-processing system that combined scanning, text recognition, and a tailored small language model. The solution extracted parts lists and relevant drawing information, standardized the output, and prepared the data for integration into AutoCAD.
Healthcare
Connecting patient acquisition and clinic administration
- 1.8×
- leads in four months
- 60%
- faster patient intake
The clinic eliminated paper forms from the implemented workflow and generated 1.8 times as many leads within the first four months. Staff reported that processing patient information digitally was at least 60% faster than the previous paper-based process.
Read the full chart
Context
A medical clinic wanted to improve the patient journey from the first advertising enquiry through appointment booking and in-clinic documentation. Its team needed a connected process that supported both patient acquisition and everyday administration.
Pain point
Fragmented systems and paper-based records created repeated data entry and administrative delays. With limited internal technology expertise, the clinic struggled to connect marketing, scheduling, and patient information into a consistent workflow.
What we did
We built a connected digital workflow linking Facebook advertising enquiries, automated follow-up, and appointment booking. At the clinic, digital intake replaced paper forms, and the information collected was organized into patient files accessible to the treating physician and the patient.
Construction
Improving visibility into construction site safety
- 0
- further safety incidents
The company reported no further safety incidents between implementation and project completion. The site was completed successfully, and the company began using the system on additional projects to support safety supervision and incident review.
Read the full chart
Context
A construction company needed a more consistent way to monitor protective equipment across a busy site. Previous incidents had highlighted gaps in the supervision of helmet, boot, and other equipment use as crews moved between work areas.
Pain point
Safety personnel could not continuously observe workers across the entire site. Missed equipment checks left unsafe situations unaddressed, while limited documentation made it difficult to establish what had happened and follow up with the people involved.
What we did
We implemented camera-based monitoring with computer vision to flag potentially missing protective equipment in monitored areas. Managers received notifications for follow-up, while recorded footage retained time-stamped flags so supervisors could review each event and address the issue with the worker involved.
Book a call
Bring us the messy version
You do not need a perfect brief. Bring the half-formed problem, the spreadsheet, or the task people repeat. We will work out whether AI belongs there.
If this is not an AI problem, we will say so on the call. We will not charge you for that.
Bring these three things
- What you want to improve
- How the work gets done today
- What a good result would look like