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GLIXlogic_
AI Solutions
Not vague AI features bolted on at the end — production-hardened LLM systems, RAG pipelines, and autonomous agents built on your data, rigorously tested, and engineered to hold up under real user traffic.
Skip the sales pitch. Speak directly with a lead engineer.
AI, Shipped to Production
From RAG pipelines grounded in your proprietary data to autonomous agents that act across your entire stack — we architect, test, and ship AI that holds up under real traffic and real edge cases.
What We Build
Every system we build is grounded in a real business problem — not a demo. Here is what we actually build and deploy.
Context-aware retrieval systems that ground your LLM in proprietary data. We optimise vector indexing, semantic scoring, and re-ranking so the model answers from your knowledge base — not from hallucination.
Production-hardened LLM features using Claude, GPT-4, Gemini, or open-source models. We implement systematic prompt engineering, output validation, and fallback logic so model behaviour is predictable under real usage.
Replace keyword matching with meaning-aware search using pgvector, Qdrant, or Pinecone. We build embedding pipelines, tune similarity thresholds, and integrate results directly into your product UX.
AI that reads invoices, contracts, reports, and PDFs — extracting structured data, classifying documents, and routing output to the right system automatically. No manual data entry.
Dashboards that don't just surface data — they interpret it. AI-generated summaries, anomaly detection, and plain-English explanations of what your metrics mean for the business right now.
Systems that process images, audio, and documents alongside text. Vision models for quality inspection, audio transcription pipelines, and unified multi-modal interfaces for complex business workflows.
Action-oriented agents equipped with custom toolsets — browsing, API calls, database writes, code execution. Engineered with memory, error recovery, and guardrails to run reliably without constant human intervention.
Coordinated fleets of specialised agents — one routes, one retrieves, one acts, one verifies. Built on LangGraph and custom orchestration layers to handle complex, multi-step business processes reliably.
Beyond rule-based automation — AI pipelines that handle exceptions intelligently, learn from patterns, and adapt to novel inputs without constant reprogramming. Built on Celery, n8n, and event-driven queues.
AI that scores, tags, and routes every incoming lead the moment it arrives — based on intent, fit, and conversation history. Integrated directly with your CRM so no opportunity falls through the cracks.
AI-powered first-response systems that resolve common issues instantly, escalate complex ones intelligently, and log everything automatically — trained on your documentation, not generic data.
AI systems that operate across your business stack autonomously — scheduling, follow-ups, report generation, data reconciliation — with a clear audit trail and human-in-the-loop checkpoints where they matter.
Systematic evaluation of model outputs using Langfuse and Promptfoo — coverage across edge cases, regression testing across prompt versions, and confidence scoring so you know exactly when the model breaks.
Safety layers that sit between the model and your users — content classification, hallucination detection, PII scrubbing, and policy enforcement. Production AI without the liability of unchecked model output.
Architecture and implementation of pgvector, Qdrant, or Pinecone — embedding pipeline design, chunking strategy, index optimisation, and retrieval tuning for production-grade semantic search and RAG.
Full observability into your AI layer — latency tracking, token cost monitoring, output quality scoring, and drift detection. Built on Langfuse so you see exactly what the model is doing in production.
Structured prompt design, chain-of-thought scaffolding, few-shot example curation, and iterative testing — turning unreliable model behaviour into consistent, production-ready output on every call.
Domain-specific fine-tuning on your proprietary data — for classification, extraction, generation, or reasoning tasks where a general-purpose model's defaults don't meet the accuracy bar your product needs.
The Difference
Transparent
We don't just build AI for clients. We use it ourselves — to build faster, test smarter, and deliver better quality on every project.
AI-assisted code generation handles boilerplate, repetitive patterns, and scaffolding — so our engineers spend their time on architecture and logic, not typing. Projects ship faster without compromising quality.
AI-powered code review catches bugs before they ship, suggests missing edge cases, and identifies security vulnerabilities that manual review would miss. Your product launches more stable.
We use large language models to accelerate specific development tasks — API integration, test suites, documentation, and data transformation logic — cutting development time without cutting corners.
AI analysis of user behaviour, heatmaps, and conversion data informs every design decision — so interfaces are optimised for real usage patterns, not just aesthetics.
AI-assisted security scanning runs continuously through development, flagging vulnerabilities in dependencies, input handling, and authentication flows before the code reaches production.
We use AI to generate initial UI variations at speed, then apply human judgement to refine and perfect them — compressing the design-to-prototype cycle from days to hours.
Try It Now
These are simplified demos of the kind of AI systems we deploy for clients. Real products are custom-built to your business and data.
Ask about our services, pricing, or process — the bot handles it.
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Want a custom AI system built specifically for your business and data?
Book an Architecture CallSkip the sales pitch. Speak directly with a lead engineer.
Everywhere, Not Just One Thing
We don't bolt AI on at the end. We architect it into the foundation of every service we offer.
