AI Development & Automation
AI development at Obsidra is building assistants, automations and LLM features on top of a company's own data, after checking that the data and the process can support them.
Scope
What this is
Applied AI engineering. We connect language models and automation to the data and tools a business already has, with guardrails, logging and an evaluation set agreed in advance. When a rule-based automation solves the problem, we build that instead of a model.
An AI system is only as reliable as the data it reads and the evaluation set it is tested against.
What is included
- AI assistants and chatbots that work in English and Arabic
- LLM integration into existing products and internal tools
- Data pipelines that prepare and keep your data usable
- Process automation across documents, email and back-office tasks
- AI readiness assessment of data, processes and risks
Pipeline
How an answer is produced
Every answer passes a check before it reaches a user, and a person can step in at any point.
Inputs
What we need from you
AI work starts once these four are in place.
- One process to start with, and the person who knows it best
- Sample documents, conversations or records from that process
- Access to the systems the assistant will read from or write to
- Agreement on what the system may and may not decide on its own
Deliverables
What you get
Each AI project ships with the evidence of how it behaves, not only the feature.
| Artefact | Form | Volume | For whom |
|---|---|---|---|
| Assistant or automation | Deployed and connected to your systems | One use case in production first | Your staff or your customers |
| Evaluation set and results | Spreadsheet and short report | Real cases with the expected result, and the outcome of every test run | Process owners and risk reviewers |
| Prompts and configuration | Files in your repository | Everything that shapes the system's behaviour | Your engineers |
| Operations guide | Written document | Monitoring, cost control and escalation | Operations staff |
Indicative timeframe: two to four weeks for a readiness assessment, six to twelve weeks for a first production use case.
Process
How we run it
Assess readiness
We review the data, the process and the risks, and say plainly whether AI is the right tool for this case.
Define the evaluation
Before building, we agree a set of real examples and what a correct result looks like for each.
Build the narrow version
One use case, connected to your systems, with logging and a clear hand-off to a person when the model is unsure.
Test on your cases
We run the evaluation set, review the failures with your team and fix them before anyone outside the project sees it.
Operate and extend
Monitoring of quality and cost in production, then the next use case on the same foundation.
Glossary
Terms on this page
- Evaluation set
- A fixed list of real examples with the correct result for each, used to test the system before and after every change.
- Retrieval-augmented generation
- A method where the model answers from passages found in your approved documents, not from its general training.
- Human in the loop
- A design where a person reviews, corrects or takes over whenever the system is unsure or a case is out of scope.
- AI readiness assessment
- A review of data, process and risk that decides whether AI is the right tool for a task.
Questions
Questions about AI development
Fit
When this is not for you
- The process itself is not written down or changes every week. Fix the process first; automating chaos makes it faster, not better.
- The data you would rely on is not accessible or not allowed to leave its current system. The readiness assessment is the right first step.
- You want AI on the roadmap but have no specific task in mind. Start with research into where the time and errors actually are.
Next step
A process AI could improve?
Tell us the decision or the system in front of you. On the intro call we say which service fits, what we would need to start, and whether we are the right team.
Book an intro call