Applied AI development: agents, vision and edge ML
Trakshym builds applied AI for operations and product teams: LLM agents and retrieval over your documents, computer vision on cameras and photos, forecasting and anomaly detection on sensor data, deployed in your cloud or on edge devices. Our engineers work remotely from India for clients worldwide and own the work from data pipeline to production monitoring.
What is applied AI?
Applied AI is machine learning built into a specific workflow with a measurable job, rather than a general-purpose model. Examples: an agent that drafts maintenance reports from work orders, a vision model that flags defects on a line, or a forecast of spare-parts demand.
Edge AI means inference runs on a gateway, camera or phone instead of in the cloud. It cuts latency and bandwidth and keeps images or sensor data on site, at the cost of smaller models and a deployment pipeline for devices.
What does Trakshym build with AI?
Models wired into real systems, with the evaluation and monitoring that show whether they are doing their job.
- 01
LLM agents and assistants
Agents that call your systems' APIs to complete tasks, with scoped tool permissions, human approval steps and a log of every action.
- 02
Retrieval over your documents
Search and question answering over manuals, tickets and policies (RAG), citing the source passage so answers can be checked.
- 03
Computer vision
Detection, classification and face or object matching on images and video, from inspection cameras to large photo libraries.
- 04
Forecasting and anomaly detection
Models on time-series data from sensors, sales or operations that predict demand or flag readings drifting from normal.
- 05
Edge deployment
Models compressed and packaged for gateways, cameras and phones, with over-the-air model updates and on-device health checks.
- 06
Evaluation and MLOps
Test sets, accuracy and cost tracking, model versioning and production monitoring, so quality is measured rather than assumed.
How do we deliver an AI project?
We agree what success looks like and measure a baseline before building, so every iteration is judged against numbers you trust.
Problem and metric
The decision or task to automate, who acts on the output, and the metric that defines good enough.
Data audit and baseline
What data exists, how it is labeled, where the gaps are, and the simplest approach that beats the current process.
Build and evaluate
Iterations on models, prompts or pipelines, each scored on a held-out test set with your team reviewing failures.
Deploy and monitor
Release to cloud or edge, with monitoring for accuracy drift, latency and cost, and a retraining or update path.
- Models
- OpenAI
- Claude
- Gemini
- Llama
- Mistral
- Whisper
- Frameworks
- PyTorch
- TensorFlow
- LangChain
- Edge runtimes
- ONNX Runtime
- TensorFlow Lite
- Linux gateways
- Mobile devices
- Serving
- Python
- FastAPI
- AWS
- Azure
- GCP
Standards and regulations we build to the requirements of
- EU AI Act: Risk-based obligations for AI systems placed on the EU market; we document to the requirements of your risk class.
What does an AI project cost, and how long does it take?
Data readiness decides it more than model choice. We size it on a discovery call and often start with a short proof of value against your data; no number before we have seen it.
Data readiness
Clean, labeled examples shorten the work; scattered or unlabeled data means building the dataset first.
Accuracy bar and risk
A suggestion a person reviews needs less validation than an action taken automatically.
Deployment target
A cloud API is quickest to ship; edge deployment adds model compression and a device update pipeline.
Usage volume
Calls per day and model size set the running cost, which shapes the choice between hosted and self-hosted models.
On a 30-minute call an engineer reviews these drivers with you and tells you what a sensible first phase would include.
Book a discovery callCommon questions about Applied AI
Should we use an LLM API or train our own model?
In most cases start with a hosted model plus retrieval over your data, because it proves value fastest. Fine-tuning or a smaller open model makes sense when cost per call, latency, data residency or a narrow task justify it, which evaluation results usually show.
Can AI models run on our edge devices?
Often, yes. Vision and anomaly models can be compressed to run on gateways, industrial PCs, cameras with accelerators or phones. We check the target hardware early, because its memory and compute decide how large the model can be.
How do you stop an LLM from making things up?
We ground answers in retrieved documents and show the source, restrict agents to approved tools, and test against real questions with known answers before launch. Where an error is costly, a person approves the action.
What data do you need to start?
Examples of the inputs and the decisions you want automated: documents, images, sensor history or tickets. Supervised models also need labels or a plan to create them. A discovery call is usually enough to tell whether your data supports the use case.
Will our data be used to train public models?
Not by us. We use providers' business APIs, which by default don't train on customer data, or host open models in your own cloud when data must not leave it. We confirm each provider's current terms during the project.
Have you shipped AI products before?
Yes, and they are live: AdPrompt.ai generates ad creative and campaign insights, GetKexy runs AI agents that research prospects and write outreach, Knvrt.ai answers from curated knowledge, and our own Fotus helps event guests find their photos in large galleries. See the portfolio for links.
Tell us what's running at your edge.
A 30-minute call with an engineer, not a salesperson. You leave with a scoped next step, whether or not we work together.