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Agentic AI Solutions

AI That Acts, Not Just Answers

We engineer agentic AI systems — autonomous agents, RAG pipelines, fine-tuned models, and intelligent automations — that transform how your business operates, not just how it communicates.

50+
AI Systems Deployed
10×
Process Automation
GPT-4o
Claude & Llama 3
RAG+
Agent Architectures
41%
Higher Completion Rates
Lendify AI loan assistant
60%
Fewer Support Tickets
post-AI agent deployment
10×
Workflow Automation
document processing speed
< 4wk
Agent MVP to Prod
average deployment time
What Are Agentic AI Solutions?

Beyond Chatbots — Intelligence That Gets Things Done

Large Language Models like GPT-4o and Claude 3.5 are not just text generators — when combined with planning architectures, memory systems, and external tools, they become autonomous agents capable of completing complex, multi-step workflows without human intervention at every step.

Agentic AI systems can browse the web, query databases, write and run code, call APIs, read and write files, and coordinate with other agents in a network — all while reasoning about the best sequence of actions to achieve a goal. This is qualitatively different from a static prompt/response interaction.

Retrieval-Augmented Generation (RAG) adds a critical capability: giving agents accurate, up-to-date access to your proprietary knowledge base without retraining the model. An agent with a well-engineered RAG layer knows your products, policies, customer history, and internal procedures at inference time.

At Abstriq, we have been building production LLM systems since GPT-3.5 — before it was mainstream. Our practice spans agent frameworks (LangGraph, CrewAI), vector infrastructure (Pinecone, pgvector), fine-tuning pipelines, and full MLOps deployment on AWS, Azure, and GCP.

45+
AI Projects Delivered
68%
Avg Automation Rate
2B+
LLM Tokens / Month
12+
Models Deployed
AI Services

Full-Stack AI Capabilities

From strategy and architecture to production deployment and monitoring — we own the full AI lifecycle.

AI Agent Development

Autonomous agents that plan, use tools, and complete multi-step tasks with minimal human intervention.

  • ReAct reasoning loops
  • Tool & API use
  • Long-horizon task execution

LLM Integration & Fine-Tuning

Connect your product to GPT-4o, Claude 3.5, Llama 3.1, or custom-trained models via secure APIs.

  • Prompt engineering
  • LoRA / QLoRA fine-tuning
  • Function calling & structured output

RAG Systems

Retrieval-Augmented Generation pipelines that give LLMs grounded access to your proprietary knowledge base.

  • Vector ingestion pipeline
  • Hybrid dense + sparse search
  • Citation & source attribution

AI Chatbots & Virtual Assistants

Conversational AI that handles customer support, internal knowledge queries, and lead qualification.

  • Multi-turn context
  • Handoff to human agents
  • WhatsApp, Slack, Web widget

Workflow Automation with AI

AI-powered automation that replaces manual, rule-based workflows with intelligent decision-making.

  • Document classification
  • Email triage automation
  • CRM auto-enrichment

Computer Vision

Image and video analysis pipelines for defect detection, OCR, object counting, and visual QA.

  • YOLO / SAM detection
  • OCR & document parsing
  • Video stream analysis

NLP Solutions

Named entity recognition, sentiment analysis, summarisation, and translation for unstructured text data.

  • Multi-language support
  • Domain-specific fine-tuning
  • Batch & real-time inference

AI Strategy & Consulting

Roadmap planning, use-case prioritisation, build vs. buy analysis, and responsible AI governance.

  • AI maturity assessment
  • ROI modelling
  • Vendor evaluation
Architecture

How an Agentic AI System Works

Six interconnected layers that give AI systems the ability to reason, remember, and act in the real world.

1

Input

User query, document, or event trigger

2

Agent Brain (LLM)

Reasoning, planning, and decision making

3

Tools & APIs

Search, code execution, external integrations

4

Memory

Short-term conversation + long-term episodic store

5

Knowledge Base

Vector DB + structured data (RAG layer)

6

Output

Verified, grounded response or autonomous action

The agent loop iterates — perceiving new information, updating its plan, selecting tools, executing actions, and observing results — until the task is complete or it escalates to a human. Each iteration is logged for full auditability and continuous improvement.

Industries

AI Transforming Every Vertical

Healthcare

Diagnostic AI & Clinical NLP

AI-assisted radiology report drafting, clinical note summarisation, patient triage chatbots, and drug interaction flagging.

Finance

Fraud Detection & Risk

Real-time transaction anomaly detection, credit underwriting automation, regulatory document review, and AI-powered trading signals.

Manufacturing

Predictive Maintenance

Sensor data anomaly detection, remaining useful life prediction, root cause analysis agents, and visual defect inspection.

Retail

Personalisation Engine

AI product recommendations, dynamic pricing optimisation, inventory forecasting, and AI-generated product descriptions at scale.

Legal

Document Review & Research

Contract clause extraction, legal research RAG, due diligence automation, and jurisdiction-specific compliance checking.

HR & Talent

Resume Screening & Matching

AI-powered JD-to-resume matching, interview question generation, candidate scoring, and bias-audited shortlisting workflows.

AI Technology Stack

Cutting-Edge AI Toolchain

Foundation LLMs

GPT-4oClaude 3.5 SonnetLlama 3.1 70BGemini 1.5 ProMistral LargeQwen 2.5

Agent Frameworks

LangChainLangGraphLlamaIndexCrewAIAutoGenSemantic Kernel

Vector Databases

PineconeWeaviateChromaDBQdrantpgvectorMilvus

ML Frameworks

PyTorchTensorFlowHugging FaceTransformersPEFTDeepSpeed

Cloud ML

AWS SageMakerAzure MLGoogle Vertex AILambda GPURunPod

MLOps

MLflowWeights & BiasesDVCBentoMLTritonPrometheus
Responsible AI

Powerful AI, Built with Integrity

We believe AI should be trustworthy by design, not by accident. These four principles are non-negotiable in every system we build.

Transparency

Every AI system we build includes explainability mechanisms — chain-of-thought logging, source citations in RAG, and audit trails for automated decisions. Stakeholders always know why the AI made a recommendation.

Fairness

We audit training data and model outputs for demographic bias using tools like Fairlearn and AI Fairness 360. Fine-tuning pipelines include bias benchmarks as acceptance criteria before production deployment.

Privacy

PII is redacted before reaching LLM APIs. Where data sensitivity requires it, we deploy open-source models on private infrastructure. All AI systems are assessed against GDPR, HIPAA, or DPDP Act requirements as applicable.

Human Oversight

High-stakes AI decisions — medical, legal, financial — always include a human-in-the-loop review step. We build confidence scoring and escalation paths into every agentic workflow so humans remain in control.

FAQ

Frequently Asked Questions

Let's Build Your AI Solution

Whether you need a focused RAG chatbot or a full autonomous agent network — we architect it, build it, and own it in production.

✦ No credit card required  ·  Response within 24 hours  ·  Free consultation