style="" AI Agents Development Services | Boxinall Softech

AI Agents

Autonomous AI Agents That Execute, Reason,
and Scale Your Operations

We design and deploy production-grade LLM agents — from single-task assistants to multi-agent orchestration systems that handle complex, multi-step business workflows autonomously.

Brief Us on Your Use Case→Explore Capabilities
40+ AI Projects Shipped
3M+ Agent Tasks Automated
95% Avg Task Accuracy
GDPR & EU AI Act Aligned

0+

AI & ML Projects Delivered

Agents, RAG systems & LLM apps

0M+

Agent Tasks Automated Monthly

Across production deployments

0+

LLM Models Integrated

OpenAI, Anthropic, Mistral & more

0%

Average Task Accuracy

Measured on production workloads

What We Build

Suite of AI Agent Development Services We Deliver

Four specialised AI engineering tracks — each scoped by agent architecture, retrieval pattern, and orchestration complexity.

LangChain / LangGraph

LLM-Powered Task Agents

Autonomous agents that plan, tool-call, and execute multi-step tasks using function-calling and ReAct reasoning loops.

  • ReAct and Plan-and-Execute agent architectures with dynamic tool selection — web search, code execution, API calls, database reads.
  • Structured output enforcement (Pydantic / Zod schemas) ensuring downstream systems receive typed, validated agent results.
  • Human-in-the-loop checkpoints with configurable approval gates for high-stakes decisions, fraud flags, or irreversible actions.
RAG / Vector Search

RAG Knowledge & Retrieval Systems

Enterprise knowledge bases with semantic search, document QA, and retrieval pipelines grounded in your proprietary data.

  • Hybrid retrieval — dense vector search (Pinecone, Weaviate) combined with BM25 keyword search for maximum recall across document types.
  • Advanced RAG patterns: HyDE, multi-query expansion, parent-child chunking, and late-interaction reranking for precision-grade retrieval.
  • Document ingestion pipelines handling PDF, DOCX, HTML, and structured data sources with metadata filtering and access-controlled retrieval.
LangGraph / AutoGen

Multi-Agent Orchestration

Supervisor-worker agent graphs where specialised sub-agents collaborate on complex tasks too broad for a single context window.

  • LangGraph stateful workflows with branching, conditional routing, and persistent memory across long-running multi-step agent executions.
  • Specialist agent pools — researcher, writer, critic, executor — orchestrated by a supervisor with shared memory and tool namespacing.
  • Idempotent agent checkpointing enabling fault-tolerant resumption of interrupted workflows without re-executing completed steps.
GPT-4 / Claude / Gemini

AI Copilots & Chat Assistants

Embedded AI assistants for products, internal tools, and customer-facing channels — context-aware, brand-safe, and measurably useful.

  • Streaming chat interfaces with WebSocket delivery, typing indicators, and citation rendering for RAG-backed responses.
  • Slack, Teams, and Telegram bot integrations with slash-command menus, interactive components, and thread-aware conversation memory.
  • Conversation analytics — intent classification, unhelpful-response flagging, and CSAT scoring — closing the quality feedback loop.

Proven Outcomes

Live AI Systems. Real Business Impact.

Production-deployed AI agents with measurable outcomes — tasks automated, accuracy rates, and latency benchmarks from live systems.

AI Agent Platform

Agentsium.ai

Enterprise agentic AI platform — custom workflows, automated business operations, and multi-agent orchestration for large organisations.

10K+Daily Agent Executions
40+Enterprise Workflows
99.5%Execution Reliability
PythonLangChainOpenAINext.js
View Case Study →

AP Automation Agent

Invoice Agent

LLM-powered AP automation agent — OCR extraction, 3-way PO matching, and intelligent dispute drafting for enterprise finance teams.

98%Match Accuracy
80%Manual Work Reduced
247/hrInvoices Processed
AWSOCRPythonLLMs

Healthcare AI Agent

KRS Medical

RAG-powered medical record summarisation agent — ingests gigabytes of patient data and produces clinically accurate summaries in seconds.

<3sSummary Latency
99%Clinical Accuracy
HIPAACompliant Architecture
RAGVector DBPythonNext.js
View Case Study →

Enterprise Conversational AI

Kore.ai

Enterprise conversational AI platform — AI assistants and automated workforce bots deployed across large-scale enterprise operations.

10M+Monthly Conversations
99.9%Platform Uptime
50+Bot Templates
NLPLLMsMicroservicesKubernetes

Industry Focus

Strategic App Engineering for High-Impact Industry Use Cases

Domain-specific AI agent design — use cases, guardrails, and accuracy requirements differ significantly across industries.

Financial Services

  • AP & AR Automation Agents
  • Fraud Detection Pipelines
  • Regulatory Filing Assistants
  • Investment Research Agents

Healthcare & Clinical

  • Medical Record Summarisation
  • Clinical Decision Support
  • Prior Authorisation Agents
  • Healthcare Coding Assistants

Legal & Compliance

  • Contract Review & Redlining
  • Due Diligence Assistants
  • Regulatory Monitoring Agents
  • E-discovery & Document QA

HR & Recruitment

  • CV Screening & Ranking Agents
  • Interview Scheduling Bots
  • Onboarding Knowledge Assistants
  • Policy & Benefits Q&A

Customer Operations

  • Tier-1 Support Automation
  • Ticket Triage & Routing
  • Knowledge Base Copilots
  • Escalation Detection Agents

Safety & Governance

Responsible AI Deployment from Day One

AI safety, data privacy, and regulatory alignment are architecture decisions — not compliance add-ons applied after deployment.

GDPR / DPDP

Data Privacy in AI Pipelines

PII redaction before LLM API calls, data minimisation in vector stores, consent-aware retrieval, and audit logs for all AI-processed personal data.

EU AI Act

EU AI Act Readiness

Risk classification for high-risk AI systems, conformity assessment documentation, human oversight mechanisms, and transparency requirements built into every deployment.

Safety & Guardrails

Hallucination & Safety Controls

Grounding verification, factual consistency checks, toxicity classifiers, and output filtering layers preventing unsafe or inaccurate agent responses reaching end users.

SOC 2

AI System Trust & Auditability

Full agent execution logging, input/output tracing (LangSmith / Arize), model version pinning, and immutable audit trails for every AI decision made in production.

Model & Framework Stack

Emerging Technology Built Into Our Engineering Stack

LLMs, vector databases, orchestration frameworks, and observability tooling — the full AI engineering ecosystem in one team.

LangChain / LangGraphLlamaIndexOpenAI GPT-4oAnthropic ClaudeGoogle GeminiPinecone / WeaviateChromaDB / QdrantAutoGen / CrewAIPython / FastAPILangSmith / ArizeRedis (Agent Memory)AWS Bedrock / GCP VertexAI

Let's Build Together

Ready to Deploy AI That Actually Works?

Tell us your use case. We'll respond with an architecture recommendation and honest feasibility assessment — no hype, no vague demos.

⚡Fast Response
🔒Fully NDA-Protected
💡Technical Insights Included

Brief Us on Your Use Case

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