What AI engineering actually looks like
Artificial Intelligence is not a product you buy off the shelf. It is an engineering discipline that requires understanding the business problem before writing a single line of code.
At Allogic, every AI project starts with a direct question: how much is this problem costing you today? If the answer justifies the investment, we build a bespoke intelligent system. If it doesn’t, we say so clearly — and we suggest alternatives.
That is what a boutique AI consultancy is: strong technical opinions, few clients, deep projects.
What we build
Predictive models trained on your proprietary data
Models trained exclusively on your operational data — not generic models retrofitted. The result is superior accuracy and a data asset that stays with the client. Applications: demand forecasting, anomaly detection, risk scoring, opportunity prioritization.
Natural Language Processing (NLP)
Structured-information extraction from unstructured text: contracts, medical records, legal documents, emails, forms. Our intelligent systems read, classify, and route documents with accuracy above 90% — no human in the default flow.
Computer Vision for inspection and recognition
Visual-pattern identification in images and video: document recognition, quality inspection, reading of scanned physical forms. Integrated directly into your process — not a lab demo.
Intelligent process automation
Unlike classic RPA, our AI-driven automations make decisions: they understand context, handle exceptions, and learn from the history of operations. The result: processes that used to require human analysts now run with minimal supervision.
AI diagnosis and roadmap
For organizations that don’t know where to start: we map processes, evaluate available data, identify where AI generates real ROI, and deliver a technical execution plan with investment and return estimates.
How a bespoke AI project works
A typical AI engineering project at Allogic has four phases:
1. Problem diagnosis (1–2 weeks) We understand the current process, map available data, and validate technical and economic feasibility. You get an honest assessment — including “not worth it right now” when that’s the truth.
2. Intelligent system architecture (1–2 weeks) We define the technical architecture: which models, which training data, how to integrate with your existing systems, how to monitor in production. A detailed proposal with phases, timeline, and projected ROI.
3. Build and validation (6–16 weeks) Short delivery cycles. You validate each component before moving forward. No surprises at the end.
4. Production and ongoing support Deploy in the client’s environment or on a managed cloud. Model-drift monitoring, periodic retraining, and direct technical support from the people who built the system.
Why not a generic AI model?
Generic models — such as off-the-shelf AI APIs — are powerful for standard use cases. But when your problem has domain-specific particulars (medical terminology, legal jargon, proprietary operational data), generic models deliver mediocre results.
A bespoke intelligent system is trained on your data, speaks the language of your industry, and is optimized for your use case. The source code and the models are the client’s assets — no dependency on an external vendor.
Sectors where we apply AI
- Healthcare and hospitals: medical-record processing, automated triage, exam analysis, privacy by design
- Insurance and brokerages: risk scoring, claims analysis, underwriting automation
- Legal and compliance: contract reading, clause extraction, obligation monitoring
- Operations and logistics: demand forecasting, anomaly detection, route optimization
Investment
Bespoke AI projects at Allogic start around USD 12,000, with typical timelines of 3 to 6 months depending on complexity. We charge for value delivered, not hours logged — aligning our incentives with the client’s outcome.
The initial conversation is free and without commitment. In 15 minutes, you’ll know whether AI fits your problem right now.