Conversational AI Agents
Handle customer-facing interactions — support, intake, qualification, onboarding — across chat, email, and messaging with multi-turn context awareness and human escalation.
AI Agent Development Services
Build autonomous software systems that perceive, decide, and act — handling claims processing, patient triage, compliance monitoring, and dispatch routing without constant human oversight. Powered by APEX, our proprietary agentic AI system.
Let's connect to help you scale fast.
50+
AI Specialists
100+
Projects Delivered
5 days
Working POC via APEX
4.9
Clutch Rating
Types of AI Agents We Build
Six agent categories covering autonomous business automation, from customer-facing conversations to multi-system orchestration.
Process Automation Agents
Execute multi-step workflows end to end — read documents, extract data, apply business rules, update systems, flag exceptions.
Decision Support Agents
Analyze data, apply domain-specific reasoning, and present recommendations so humans make the final call with better information.
Multi-Agent Orchestration Systems
Teams of specialized agents coordinating to solve problems no single agent can handle — claims adjudication, KYC, supply chain.
Voice AI Agents
Phone-based agents for inbound service, outbound reminders, dispatch coordination, and after-hours intake — telephony-integrated and context-aware.
Data Analysis Agents
Continuously monitor systems, detect anomalies, generate reports, and surface insights in real time across fraud, clinical, and operational tracking.
Industry-Specific AI Agent Use Cases
Dispatch Optimization
Real-time capacity analysis, route assignment, and driver matching for optimal delivery.
Carrier Management
Rate negotiation, capacity booking, and carrier performance tracking at scale.
Exception Handling
Auto-rerouting, customer notification, and ETA updates when disruptions happen.
Shipment Tracking
Multi-carrier unified visibility for customers and operations teams.
How APEX Accelerates AI Agent Development
75% of enterprise AI agent projects fail to move from pilot to production (Forrester). Most teams build from scratch every time — reinventing orchestration, memory management, evaluation, and deployment for each project.
APEX is not a wrapper around LangChain. It's a production-tested architecture with structured quality gates, built-in agent communication patterns, and monitoring infrastructure refined across 100+ projects.
When you build with APEX, you skip 60–70% of the infrastructure effort and focus on your domain logic.
5 days
Working POC via APEX
2 weeks
Production agent via APEX
60-70%
Less development effort
3-6x
Faster than scratch
Our AI Agent Development Process
- 1
Agent Discovery & Use Case Mapping
Map the business process, identify highest-impact automation targets, quantify ROI, and define success metrics before touching technology.
- 2
Architecture Design & Tool Selection
Single-agent vs. multi-agent, model selection, tool integrations, memory strategy, and deployment approach — decided before development.
- 3
Agent Development & Training
Build using APEX with parallel workstreams — agent logic, data pipelines, and integrations move simultaneously. Weekly working demos.
- 4
Integration with Existing Systems
Connect to CRMs, ERPs, databases, and third-party APIs using MCP (Model Context Protocol) and A2A for agent-to-agent communication.
- 5
Testing, Quality Gates & Deployment
Structured quality gates: accuracy, hallucination rates, edge cases, latency, security. Evaluated against your specific benchmarks.
- 6
Monitoring, Learning & Optimization
Continuous performance monitoring, cost tracking, feedback loops, and agent retraining. Production is the beginning, not the end.
Technology Stack
- LLMs
GPT
Claude
Gemini Pro
Llama
Mistral
Command R+
- Agent Frameworks
LangChain
LangGraph
CrewAI- AutoGen
Semantic Kernel
Haystack
- Vector Databases
Pinecone- Weaviate
Qdrant
Chroma
pgvector
Milvus
- Orchestration
Apache Airflow
Temporal
Prefect
Kubernetes
Docker
- Cloud AI
AWS Bedrock
Azure OpenAI
Google Vertex AI
Hugging Face
- Protocols
MCP (Model Context Protocol)- A2A (Agent-to-Agent)
- REST
GraphQL- gRPC
- Monitoring
- LangSmith
Weights & Biases
Datadog
Prometheus
Grafana
- Security
- Guardrails AI
NeMo Guardrails- RBAC
SOC 2
HIPAA
GDPR compliance
If our estimate is wrong, that's our problem, not your invoice.
That's Softermii APEX: production-ready software for one fixed price, agreed before we start, and you own every line. See the packs and the exact prices.
Why Companies Choose Softermii
| Criterion | Softermii (APEX) | Generic AI Agency | Building In-House |
|---|---|---|---|
| Time to POC | 5 days | 2–4 weeks | 4–12 weeks |
| Time to Production | 2–6 weeks | 2–4 months | 3–6 months |
| Domain Expertise | Insurance, fintech, healthcare, logistics — 100+ projects | Varies, often generalist | Your domain, no AI experience |
| Proprietary Tech | APEX agentic system | Open-source only | Open-source only |
| Scope Guarantee | Fixed-scope, no open-ended billing | T&M, scope creep | No external accountability |
| Post-Launch | Monitoring, optimization, retraining | Handoff with docs | Fully on your team |
| Certifications | AWS, Microsoft, IBM, Google | Varies | Depends on hires |
Case Studies
Insurance Claims Processing Agent
Problem
Mid-market carrier — 4 adjusters, 6+ hours/day on manual document review. Average cycle: 11 days per claim.
Agent Solution
Multi-agent APEX system: FNOL intake agent, damage assessment agent, adjuster assignment agent.
11→3 days
Cycle time
3x
Volume capacity
4 months
ROI payback
Fintech KYC Automation Agent
Problem
Digital lending platform losing 35% of applicants during 48–72 hour KYC onboarding process.
Agent Solution
Single-agent KYC system: identity validation, sanctions screening, risk scoring, compliance checks.
72h→15m
Processing time
-28%
Abandonment
88%
Auto-approved
Most AI agent projects fail not because the technology doesn't work, but because teams build agents that solve the wrong problem or can't handle real-world edge cases. APEX exists to eliminate both risks — we validate the use case with a working proof of concept before committing to full development, and our production framework handles the messy reality of enterprise environments.
CEO & Co-Founder, Softermii
Slava Vaniukov
Frequently Asked Questions
What is an AI agent and how is it different from a chatbot?
A chatbot follows predefined scripts and responds to user inputs within a narrow set of rules. An AI agent is autonomous — it can reason about tasks, use tools, access external systems, and take multi-step actions to achieve a goal without being told each step. A chatbot answers questions. An agent processes a claim, updates three systems, sends a notification, and flags exceptions — on its own.
How much does AI agent development cost?
It depends on scope — single-agent vs multi-agent, the number of integrations, and how well-defined the process is. Rather than hourly rates or open-ended ranges, every Softermii project is delivered for one fixed price, agreed before we start. With APEX you can start with a working proof of concept to validate feasibility before committing to a full build. See the APEX packs and exact prices at /apex/pricing.
How long does it take to build a custom AI agent?
Using APEX, a working proof of concept takes 5 days. A production-ready single agent takes 2–8 weeks. Multi-agent systems take 1–3 months. Enterprise deployments take 3+ months. These timelines are 40–60% shorter than building from scratch because APEX provides the infrastructure layer out of the box.
Can AI agents integrate with our existing software?
Yes. AI agents are designed to work with your current systems, not replace them. We integrate with CRMs (Salesforce, HubSpot), ERPs (SAP, NetSuite), databases, document management systems, communication platforms, and any system with an API. We use MCP (Model Context Protocol) and standard REST/GraphQL integrations to connect agents to your tech stack.
What industries benefit most from AI agents?
Industries with high-volume, rule-heavy processes see the fastest ROI: insurance (claims, underwriting), fintech (KYC, compliance), healthcare (triage, scheduling, documentation), and logistics (dispatch, tracking, exception handling). The key factor is whether the process is well-defined enough to automate but too complex for traditional rules-based software.
How do you ensure AI agent reliability in production?
We use APEX's built-in evaluation framework to measure accuracy, hallucination rates, latency, and task completion rates against your specific benchmarks. Every agent goes through structured quality gates before deployment. In production, we monitor performance continuously, set up alerting for anomalies, and implement human-in-the-loop fallbacks for edge cases.
What's the difference between single-agent and multi-agent systems?
A single-agent system handles one task or workflow. A multi-agent system uses multiple specialized agents that coordinate with each other to handle complex, end-to-end processes. Multi-agent systems are 3–5x more complex and costly, so we recommend them only when the workflow genuinely requires multiple types of reasoning or coordination.
Do we own the code and IP?
Yes. You own 100% of the custom code, trained models, and intellectual property we build for you. The APEX platform components are licensed, but your domain-specific configurations, custom integrations, and trained models are entirely yours. You can deploy on your own infrastructure and maintain full control of your data.
Ready to Build AI Agents That Actually Work?
Tell us the process you want to automate. We will assess feasibility, recommend an architecture, and provide a fixed-scope proposal within 5 business days.
Don't Dream for Success, Let Us Make It Real
Tell us what you're building. We'll tell you how fast we can ship it — and what it'll cost.
Have your project done faster with our AI-agent system 