We build AI agents that do real work. Not chatbots that parrot FAQ answers; not demos that fall apart on your first edge case. Since 2014, our team of 20+ AI/ML engineers has shipped agents that replace manual processes, close deals, screen candidates, and run entire workflows without human babysitting.

An AI agent is software that perceives its environment, decides what to do, and takes action toward a goal. Unlike a chatbot that follows a script, an agent uses tools, holds memory across sessions, breaks complex tasks into steps, and course-corrects when something goes wrong.

We’ve delivered 250+ projects across 12 years. The AI agents we ship today are the most impactful work we’ve done.

What types of AI agents do we build?

Six categories, each mapped to a real business function with measurable ROI.

Customer service agents

These agents handle Tier 1 and Tier 2 support tickets across email, chat, and voice. They pull context from your CRM, check order status in your backend, process returns, and escalate to a human only when the situation actually calls for one.

One client cut their support headcount from 12 to 4 while improving first-response time from 4 hours to 11 seconds. The agent handles 73% of inbound tickets end-to-end. (For voice-specific deployments, see our voice support agents page.)

Sales agents

Outbound calling, lead qualification, appointment scheduling, follow-up sequences. Our sales agents integrate with your CRM, update deal stages in real time, and book meetings directly on your reps’ calendars.

For a real estate client, we built a voice sales agent that replaced 3 full-time SDRs. The agent makes 400+ calls per day, qualifies leads against 14 criteria, and books showings without human involvement.

Data analysis agents

These agents connect to your databases, data warehouses, and APIs. You ask questions in plain English; the agent writes SQL, runs queries, generates visualizations, and delivers reports. No more waiting three days for the analytics team to pull numbers.

Workflow automation agents

Multi-step business processes that currently live in spreadsheets, Slack threads, and someone’s memory. Our agents orchestrate approvals, route documents, fire notifications, update systems, and handle the exception cases. They replace the duct-tape automation that breaks every time someone renames a field.

Recruitment agents

Resume screening, candidate scoring, interview scheduling, and initial outreach. We built a recruitment agent that took a client’s screening process from 3 full-time recruiters down to 1 recruiter overseeing the agent. Time-to-shortlist dropped from 5 days to 6 hours.

Coding agents

Agents that write code, run tests, fix bugs, and submit pull requests. We deploy these internally and build them for engineering teams that want to accelerate development cycles. They work best on well-defined tasks: migration scripts, test generation, boilerplate, bug triage.

How our AI agents actually work

Every agent we build follows the same core architecture. Understanding it helps you tell what’s real from what’s marketing fluff.

The agent loop

An agent runs in a loop: Perceive → Plan → Act → Observe → Repeat.

  1. Perceive. The agent receives input: a customer message, a new lead in your CRM, a scheduled trigger, a data change.
  2. Plan. The LLM reasons about what to do next. For complex tasks, it breaks the goal into subtasks. This is where prompt engineering and system design matter most.
  3. Act. The agent calls tools: APIs, databases, file systems, web browsers, other agents. Tools are what separate an agent from a chatbot.
  4. Observe. The agent evaluates the result. Did the API call succeed? Does the data make sense? Is the customer satisfied?
  5. Repeat. If the goal isn’t met, the agent loops back to planning with the new context.

Memory

Short-term memory (conversation context) and long-term memory (vector stores, databases) let agents hold state across sessions. When your customer calls back three weeks later, the agent remembers the previous interaction, the resolution, and their preferences. (We cover the long-term memory side in detail on our RAG development page.)

Tools

Tools are the agent’s hands. We integrate agents with:

  • APIs. Your CRM, ERP, ticketing system, payment processor, calendar.
  • Databases. Direct SQL access for reads and controlled writes.
  • Web browsers. For scraping, form filling, and navigating web apps.
  • File systems. Reading documents, generating reports, processing uploads.
  • Other agents. Multi-agent systems where specialized agents collaborate.

Guardrails

Every agent ships with output validation, rate limiting, human-in-the-loop checkpoints for high-stakes decisions, and comprehensive logging. We don’t build agents that can go rogue.

Our tech stack

We’re not locked into one framework. We pick the right tool for the problem.

Agent frameworks

Framework Best for When we use it
LangChain / LangGraph Complex chains, stateful workflows Most projects. Mature ecosystem, excellent tool integration.
CrewAI Multi-agent collaboration When you need specialized agents working together on a shared goal.
AutoGen Conversational multi-agent Research-heavy tasks, code generation, iterative problem-solving.
OpenAI Swarm Lightweight agent handoffs Simple routing between specialized agents.
Custom Python Full control, minimal overhead When frameworks add complexity without value.

LLMs we deploy

We’re model-agnostic. Your agent shouldn’t be locked to one provider.

  • GPT-4o / GPT-4o-mini. Best general-purpose reasoning, strong tool use.
  • Claude 3.5 Sonnet / Claude 3 Opus. Excellent for long-context tasks and nuanced analysis.
  • Gemini 1.5 Pro. 1M+ token context window, great for document-heavy workflows.
  • Llama 3.1 (70B/405B). Self-hosted option for data-sensitive environments.
  • DeepSeek V3. Strong reasoning at lower cost.
  • Mistral Large. European hosting, good multilingual performance.

We design agents so the model can be swapped without rewriting code. If OpenAI raises prices or a better model launches, your agent adapts.

Ready to discuss your AI agent project? Talk to our engineering team.

AI agent vs. chatbot vs. RPA: what’s the difference?

This question comes up in every discovery call.

Capability AI Agent Chatbot RPA
Decision-making Reasons about novel situations Follows scripted paths Follows recorded macros
Tool usage Calls APIs, queries databases, browses web Limited to pre-built integrations Mimics mouse clicks and keystrokes
Memory Maintains context across sessions Resets each conversation (usually) No memory
Handles exceptions Adapts and retries with new approaches Falls back to human Breaks and alerts
Setup time 4-12 weeks 1-4 weeks 2-8 weeks
Maintenance Prompt tuning, tool updates Script updates Breaks when UI changes
Best for Complex, judgment-heavy tasks Simple Q&A, FAQ Repetitive, rule-based data entry

Our recommendation. Most businesses need a combination. Use chatbots for simple FAQ. Use RPA for data entry between legacy systems. Use AI agents for everything that takes judgment, context, or multi-step reasoning.

What does an AI agent cost?

We quote every project individually, but here are realistic ranges based on projects we’ve actually delivered.

Simple single-agent systems: $20,000 – $40,000

  • One agent, one domain (e.g., customer support only)
  • 3-5 tool integrations
  • Basic memory (conversation history)
  • Standard guardrails
  • 6-8 week delivery

Multi-agent systems: $60,000 – $120,000

  • Multiple specialized agents collaborating
  • 10+ tool integrations
  • Long-term memory with vector stores
  • Custom evaluation and monitoring dashboards
  • Human-in-the-loop workflows
  • 12-20 week delivery

Enterprise deployments: $120,000+

  • Organization-wide agent infrastructure
  • Custom model fine-tuning
  • On-premise or private cloud deployment
  • SSO, RBAC, audit logging
  • Ongoing optimization and support
  • 20-40 week delivery

These are real project costs, not aspirational pricing. The biggest variable is integration complexity. Connecting to a modern REST API takes days. Connecting to a 15-year-old SOAP service with no documentation takes weeks.

For comparison, US-based agencies typically charge $200-$400/hour. Our rates deliver 60-70% cost savings without compromising on engineering quality. Our team in Lahore works your timezone, with a US office in Danville, California for local coordination.

Our development process

Week 1-2: Discovery and architecture

We map your current workflow, identify automation candidates, and design the agent architecture. You get a technical spec with system diagrams, tool definitions, LLM selection rationale, and a test plan. (More detail on our end-to-end process if you want it.)

Week 3-6: Build and iterate

Two-week sprints. You see working demos at the end of each sprint. We test with your real data from day one. Synthetic data hides integration problems.

Week 7-8: Hardening and deployment

Load testing, edge case coverage, guardrail validation, monitoring setup. We deploy to your infrastructure or manage hosting for you.

Ongoing: monitor and optimize

Agents improve over time. We track success rates, failure modes, and user satisfaction. Monthly optimization cycles adjust prompts, add tools, and update models.

Get a detailed estimate for your project. No sales pitch, just engineering.

Industries we’ve built agents for

  • EdTech. Skolaro (1.5M+ users), with student support agents and grading automation.
  • FinTech. Metabeta, with investment analysis agents and portfolio monitoring.
  • Real Estate. Property matching agents, showing schedulers, lead qualifiers.
  • E-commerce. Order management, returns processing, inventory alerts.
  • Healthcare. Appointment scheduling, symptom triage, insurance verification.
  • Legal. Document review agents, contract analysis, compliance monitoring.
  • HR / Recruiting. Resume screening, interview scheduling, onboarding workflows.

Why companies choose Contrive for AI agent development

12 years of delivery. We’ve been shipping software since 2014. AI agents are our latest capability, built on a foundation of 250+ delivered projects.

20+ AI/ML engineers. Not a two-person shop that outsources to freelancers. Our team includes ML engineers, NLP specialists, and full-stack developers who’ve worked with LLMs since GPT-3.

We build our own AI products. Our voice agent platform (voice.agent.contrivesol.com) runs in production. We use what we sell.

95% client retention. Clients come back because we deliver. 98% on-time delivery across all projects.

Rated 4.9/5 on Indeed, 4.7/5 on Clutch, 4.5/5 on Glassdoor. The team ratings show up in the work clients receive.

Frequently Asked Questions

How long does it take to build a custom AI agent?

A single-purpose agent with 3-5 integrations typically takes 6-8 weeks from kickoff to production. Multi-agent systems with complex orchestration run 12-20 weeks. The biggest time factor is integration work: connecting to your existing systems, handling authentication, mapping data formats. We deliver working demos every two weeks so you see progress early.

Can AI agents work with our existing software (Salesforce, HubSpot, SAP, etc.)?

Yes. Every agent we build connects to your existing stack through APIs. We’ve integrated with Salesforce, HubSpot, Zendesk, Jira, SAP, Slack, Microsoft Teams, Google Workspace, and dozens of custom backends. If your software has an API, or even a database we can query, we can connect an agent to it.

What happens when the agent encounters something it can’t handle?

We build explicit escalation paths into every agent. When confidence drops below a threshold, when the task falls outside the agent’s domain, or when a human-in-the-loop checkpoint is reached, the agent routes to a human with full context. The human never starts from scratch. They get the conversation history, the agent’s reasoning, and recommended actions.

Is our data safe? Can we host agents on our own infrastructure?

Absolutely. We offer three deployment models: cloud-hosted (we manage everything), your cloud (AWS, GCP, Azure, deployed to your account), and on-premise. For data-sensitive environments, we deploy open-source models like Llama 3.1 that never send data to external APIs. All agents include encryption at rest and in transit, role-based access control, and audit logging.

How do AI agents differ from the chatbot we already have?

Your chatbot probably follows a decision tree or retrieves answers from a knowledge base. An AI agent can use tools (call APIs, query databases, take actions in other software), maintain memory across conversations, break complex tasks into subtasks, and handle situations it hasn’t been explicitly programmed for. A chatbot tells your customer their order status. An agent checks the status, sees the shipment is delayed, contacts the carrier, updates the customer, and offers a discount, all without human intervention.

What’s the ROI of an AI agent vs. hiring more staff?

For a customer service agent handling 500 tickets/day, we typically see 60-80% automation of Tier 1 tickets within the first month. At an average fully-loaded cost of $45,000/year per support rep, automating the work of 3-4 reps pays for the agent build in 4-6 months. After that, the marginal cost of handling additional volume is near zero. The agent scales without hiring. Our recruitment automation project replaced the workload of 3 FTEs with 1 FTE overseeing the agent, saving roughly $180,000/year in labor costs.

Contrive Solutions has been building software since 2014. Our team of 66 professionals, including 20+ AI/ML engineers, works from our headquarters in Lahore, Pakistan and our US office in Danville, California. Start a conversation about your AI agent project. Or call us: +1 (775) 459-7713 (US) or 042 35199410 / +92 327 4945650 (Pakistan).