Engineering™ · AI & Automation

AI Agents

We build autonomous AI agents connected to your databases and tools to execute real operational tasks.

Custom autonomous agents with tool-calling capabilities, persistent memory, and live execution in your systems. These are not conversational toys: they are specialized technical assistants that resolve actual business jobs.

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Scope

Custom Autonomous Agent · Function Calling & Tools · Contextual Memory Systems · Safe API Execution

Estimated Timeline

4–7 weeks

Platforms

LangGraph / LlamaIndex · OpenAI / Claude / Gemini · Qdrant / Pinecone · Custom APIs

Target Fit

Is this for your company?

This service is for you if

✓Employees spend hours hunting for technical procedures buried across manuals, Notion, or internal chats.
✓You want a commercial agent that qualifies leads, verifies real-time stock, and books CRM calendar meetings.
✓Operations teams need an assistant that converts natural language questions into complex database queries.
✓You need to automate Tier-1 customer support with reliable answers strictly sourced from technical knowledge bases.

You probably do not need it if

✕You only need a static FAQ chatbot without tool execution or dynamic database retrieval.
✕You lack internal documentation or structured knowledge to ground the agent.

Problem Space

What we solve

Safe Function & Tool Calling

Autonomous agents capable of calling API endpoints to check balances, issue tickets, schedule meetings, or dispatch emails.

Zero-Hallucination Knowledge Retrieval (RAG)

Advanced retrieval-augmented generation architectures grounding every response exclusively in verified internal documentation.

Stateful Contextual Memory

Agents remember previous user interactions, customer preferences, and process states across sessions.

Enterprise Safety Guardrails

Strict behavioral guardrails preventing role leakage, confidential data disclosure, or unauthorized system actions.

Engineering Process

How it works

01 — Diagnose

Role Scoping & System Permissions

We define the agent persona, available tool endpoints, knowledge boundaries, and operational permissions.

02 — Design

RAG Architecture & Tool Schemas

We vectorize private documentation and code OpenAPI function specifications for all accessible tools.

03 — Build

Reasoning Loops & Precision Testing

We program agent decision graphs, test tool-calling reliability, and calibrate system prompts.

04 — Launch

Channel Integration & Observability

We deploy the agent to Slack, WhatsApp, web, or internal portals with real-time audit logging.

Deliverables

What we deliver

Upon completion you will have

✓Specialized AI agent deployed on secure cloud infrastructure.

✓Vector knowledge base synced with internal company documentation.

✓Functional integration with up to 4 operational tools (APIs, CRM, or database).

✓Anti-hallucination guardrails and safety evaluation suite.

✓Observability dashboard tracking latency, user satisfaction, and tool execution logs.

Delivery Plan

Implementation Phases

Tiempo típico de proyecto:4–7 weeks

Week 1Phase 1

Objective Scoping & Data Collection

Knowledge base gathering, API tool specification, and credential provisioning.

Weeks 2–3Phase 2

Vector Pipeline & Tool Engineering

Document embedding, vector store setup, system prompt design, and function calling development.

Weeks 4–5Phase 3

Safety Guardrails & Accuracy Benchmarking

Adversarial prompt testing, tone calibration, and end-to-end tool execution validation.

Weeks 6–7Phase 4

Channel Rollout & Team Onboarding

Deployment to Slack/WhatsApp/Web, user training, and adoption monitoring.

Pricing Guidance

Estimated Investment

Target Investment

USD 4,800

Includes agent engineering, vector RAG pipeline, tool API connections, and observability dashboard.

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Real-World Proof

Impact Case Study

From Overwhelmed Support Desk to 70% First-Contact Instant Resolution

Initial problem

Telecom company with 40 support reps overwhelmed by 8,000 repetitive monthly technical inquiries buried in 300-page manuals.

Technical intervention

Development of an AI technical agent grounded in Qdrant vector knowledge with tools to check customer router diagnostic states via API.

Outcome achieved

Agent autonomously resolved 71% of inbound inquiries in under 10 seconds, dropping average customer queue wait times from 25 minutes to zero.

Clarifications

Frequently asked questions

How do you prevent the AI agent from hallucinating false information?

We implement advanced RAG with strict grounding: the agent is instructed to answer strictly using retrieved factual chunks. If the answer is not present in the verified documentation, it explicitly states it does not know and escalates to a human.

Where can the agent be deployed?

Across whatever channels your team or customers already use: embeddable web widgets, internal Slack/Teams bots, or official WhatsApp Business API numbers.

Which underlying LLMs power the agent?

We select the optimal model for each task: Claude 3.5 Sonnet for complex reasoning, GPT-4o for fast tool calling, or open-weight models deployed in private clouds for complete data sovereignty.

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