Accelerate the SDLC
From requirement to deployment — faster cycles through AI-assisted design, coding and testing.
Domain · AI
AI that moves enterprise software forward.
In an era of digital transformation and surging demand for software, Blameo provides a comprehensive AI solution suite — engineered to accelerate the SDLC, reduce OpEx, elevate deliverable quality, and optimise development resources. Below: seven production case studies across code intelligence, conversational AI, vision and operations.
What our AI suite delivers
Four outcomes that decide AI ROI.
Reduce OpEx
Automate repetitive operational and maintenance work — freeing senior talent for strategic outcomes.
Elevate quality
AI-augmented testing, code review and documentation lift the floor and ceiling of every release.
Optimise team resources
Right-size engineering teams by augmenting them with AI agents that handle scaffolding, refactoring and ops.
Case study · 01
Codense — turn any codebase into a knowledge graph.
Codense is an intelligent source-code analysis platform that transforms codebases into a Knowledge Graph — enriched with metadata tailored to specific use cases. Four core capabilities ship in the box:
- Code graph analysis. Visualises source-code structure and relationships between components.
- Reverse engineering. Automatically generates design documentation from existing source (Source → Design).
- Code comprehension. Lets engineers grasp large-scale, complex codebases rapidly.
- Optimisation suggestions. Recommends improvements based on industry best practices and patterns.
Case study · 02
Coding Agent — automation that sits inside the IDE.
An agent that assists and automates the daily coding workflow — without removing the engineer from the loop.
- Design-to-code. Generate working code directly from design documents.
- Intelligent completion. Context-aware suggestions that respect your codebase conventions.
- Refactoring assistance. Safe, repo-scale refactors with explanation.
- Bug-fix recommendations. Surface the likely fix when the test goes red — with the reasoning.
Case study · 03
Platform transformation — legacy to modern, accelerated by AI.
Our Platform Transformation solution uses AI to optimise and accelerate migration across languages, platforms/OS, and software architectures.
From: COBOL, VB6/VB.NET, Delphi, C/C++ (legacy), Windows Desktop, Mainframe, on-premise, monolithic, client-server, layered systems.
To: Java/Kotlin, Python, Go/Rust, TypeScript, Android, iOS, Cloud Native, microservices, event-driven, clean/hexagonal architecture.
- AI-assisted code translation. Programming language migration with human review built into the pipeline.
- Architectural pattern mapping. Monolith → microservices with clear boundary recommendations.
- Continuous validation. Tests generated from legacy behaviour to ensure post-migration parity.
Case study · 04
AMI Chatbot — interactive AI assistant for PTIT.
Universities deal with thousands of admissions and academic queries every cycle. PTIT needed accurate information retrieval and a richer interface than a search box.
- Problem. Accurate retrieval needed for admissions and academic queries.
- Solution. AI-powered chatbot with a 2D interactive interface and an LLM-RAG (Retrieval-Augmented Generation) pipeline.
- Implementation. Secure conversational pipelines ensure sensitive student data is protected.
- Outcome. Accurate, secure, real-time student information retrieval with strong privacy controls.
Case study · 05
Computer vision for workplace safety.
Modern workplaces face increasing automation and human-machine collaboration — both demanding safety supervision that scales beyond manual oversight. We built a vision system that watches the floor and reasons about what it sees.
What it detects:
- Safety gear compliance. Verifies workers are wearing required PPE (helmets, gloves, vests). Alerts when gear is missing.
- Workforce monitoring. Efficient counting and movement tracking for operational efficiency and resource management.
- Behavioural insights. Detects unsafe behaviour early — before accidents and disruptions cascade.
The outcome: improved safety, minimised risk of penalties, and verified adherence to safety protocols — without a permanent staff increase.
Case study · 06
KnowledgeHub — ask questions, not keywords.
Internal search across documents, spreadsheets and PDFs has been broken for two decades. KnowledgeHub treats it as a knowledge-graph problem — with retrieval grounded in your data, not the public internet.
- Upload your sources. Word docs, spreadsheets, PDFs and more — ingested and enriched as a knowledge graph.
- Ask AI by concept, not keyword. Precise, content-aware answers grounded in your data — with citations.
- Works across all your BI. From legal contracts to engineering specs, KnowledgeHub processes information at scale with human-like understanding.
- Built for enterprise teams. Scales from small teams to entire enterprises with role-based access.
Case study · 07
iWMS — an intelligent warehouse, end to end.
Modern warehouses are heterogeneous — multiple hardware classes, multiple software layers, and human operators in between. iWMS unifies them into one operational system.
- Comprehensive integration. Connects the entire heterogeneous warehouse stack — hardware through software.
- Full automation. Material handling via AMRs (Autonomous Mobile Robots), FMRs (Forklift Mobile Robots), and robotic arms — minimising manual labour.
- Optimised workflow. Streamlined order fulfilment, real-time updates, fewer errors.
- Advanced hardware integration. Conveyors, buffer racks, put-to-light systems, depalletisers, DWS (Dimension Weight Scanning) — orchestrated as one.
Example deployment: a 1,944 m² warehouse with 10,644 storage positions, 20 CTUs, 5 charging stations, supporting LCL (Less than Container Load) shipments at 100 boxes/hour per station, inbound at 50 picking/hour across four stations, outbound at 100 picking/hour across three stations.
An AI idea that needs to ship — not just demo?
From a single AI feature to a multi-system platform, we'll bring the engineering rigour that lets AI projects survive production.