Python Annotations
LANCER
Lancer :
Ideal for quick, focused engagements
Team: ≤ 2 resources
Duration: ≤ 3 weeks
Indicative Budget: ≤ $3,000 USD*
Use Cases: MVPs, prototypes, simple websites, bug-fix sprints
Indicative Pricing: ~$15–$25/hr per resource*
Growth :
Best suited for growing products or structured builds
Team: 2–5 resources
Duration: Up to 4 months
Indicative Budget: Up to $30,000 USD*
Use Cases: Feature-rich web apps, integrations, custom dashboards
Indicative Pricing: ~$15–$35/hr per resource*
Enterprise :
Tailored for high-scale, complex or long-term initiatives
Team: 5+ resources
Duration: More than 4 months
Indicative Budget: Starts from $30,000+ USD*
Use Cases: End-to-end platforms, SaaS products, compliance-heavy systems
Indicative Pricing: ~$25–$50/hr depending on roles*
(Development)
Project Description
A global AI-driven product company specializing in natural language processing aimed to simplify logic generation through human-like commands. They approached Oodles to support a project where sentences in plain English needed to be transformed into executable Python expressions using a fixed library of functions for downstream machine understanding and automation tasks.
Scope Of Work
The project focused on converting natural language inputs into precise Python logic using predefined function sets. With the help of Oodles, the client needed support with expression construction, language mapping, and code validation. Key areas included logic design, Python implementation, and linguistic decomposition of user prompts for automation workflows.
Our Solution
To meet the client’s unique requirements, the implementation strategy revolved around using Python 3 and a controlled vocabulary of functional expressions.
Implementation Highlights:
- Functional Library Familiarization: The team studied and practiced with a fixed set of Python functions (like exist, filter_obj, and lambda).
- Sentence Decomposition: Each English sentence was broken down into components such as quantifiers, conditions, and objects—making it easier to translate into clean, readable code.
- Logic Mapping and Expression Building: Sentences were translated into exist(filter_obj(all_items, lambda x: is_yellow(x))).
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