Email:info@softosmith.com
Working Hours:Mon - Fri: 9 AM - 5 PM (PKT)
We design and build AI agents around your actual business processes — single-agent or coordinated multi-agent systems, built on real orchestration frameworks, secure integrations, and delivery that scales with you.
A lot of agencies selling "AI agents" are reselling a no-code platform with a prompt pasted in. That works for a demo, but it breaks down the moment the task needs real reasoning, real tool access, or real reliability.
A single prompt in a no-code tool, sold as a custom agent, with no real architecture behind it.
Agents that can describe an action but can't actually call your systems to perform it.
Output that works in a demo and falls apart the first time a real user asks something unexpected.
Real AI agent development means designing the orchestration layer, building retrieval so the agent reasons over your actual data, wiring in tool-calling so it can take real action, and testing it against real scenarios before it ever touches production.
Everything needed to take an AI agent from an idea to something running reliably in production.
Agent design scoped to the actual task — single-agent, multi-agent, or a hybrid workflow — instead of forcing every use case into one template.
Coordinated agents built with CrewAI-style patterns, where separate agents handle separate parts of a workflow and hand off cleanly.
Retrieval pipelines that ground agent responses in your own documents and data through vector search.
Function-calling built against your real APIs, so the agent can actually take action, not just describe what it would do.
Prompt structure tested against real scenarios with an evaluation process, not a single prompt shipped and hoped for the best.
Agents deployed to your own infrastructure or a managed environment, with monitoring in place from launch.
Every build starts with mapping the actual task, then choosing the right orchestration framework — LangChain for a straightforward pipeline, LangGraph for stateful multi-step reasoning, or CrewAI when the work is better split across coordinated agents. We layer in RAG so the agent reasons over your real data, wire tool-calling into your systems so it can take action, and run evaluation against real scenarios before anything ships — with guardrails in place so the agent fails safely instead of silently.
This is the same architecture used across every agent we build, whether it's conversational, voice, or a backend automation agent.
LangChain, LangGraph, and CrewAI used to build reliable single-agent and multi-agent workflows, chosen based on what the task actually needs.
Retrieval pipelines on Pinecone, Qdrant, or pgvector, so agents reason over your real data instead of the model's training data alone.
Structured tool definitions that let agents call your APIs to look up, create, or update real records.
Test cases, output evaluation, and logging built into the agent so behavior can be verified and traced, not treated as a black box.
Agents deployed with scoped credentials and proper secrets management, on your infrastructure or a managed environment.
A clear, six-step process from discovery to ongoing support.
We review the business process and map exactly what the agent needs to know, decide, and do.
Orchestration framework, agent structure, and tool list scoped and agreed before development starts.
The agent built against real APIs and data, version-controlled, with guardrails from the start.
Tested against real scenarios with a structured evaluation process before anything goes live.
Rolled out to production with monitoring and logging in place from day one.
Ongoing tuning as usage patterns and requirements change.
Internal and customer-facing agents that automate repetitive product and support workflows.
Intake, research, and reporting agents that save hours of manual work per week.
Inventory, order, and customer service agents connected to your store and back-office systems.
Enrollment, grading support, and content agents connected to your LMS, including Moodle where relevant.
Administrative and intake agents built with compliance-aware handling of patient information.
Lead research, qualification, and follow-up agents that work through your existing pipeline.
Agents that complete an actual task end to end, not just answer a question.
Repetitive workflows handled without adding headcount for routine work.
A fixed-scope build gets a working agent into production in weeks, not a multi-month platform rollout.
Architecture designed to handle more volume, more tools, and more agents as your needs grow.
No client logos or star ratings here — just the commitments every agent build is held to.
You work directly with the engineer building your agent — no account managers relaying messages back and forth.
The quote we give you upfront is what you pay. No surprise invoices once development starts.
Softosmith is run by Amin Ali, Founder & Lead AI Engineer with 8+ years building automation pipelines and AI agent/RAG systems, BSc CS from Virtual University of Pakistan — your agent is architected by the person who actually understands the frameworks underneath it.
The agent's architecture, prompts, and integration code are yours. Nothing rented or locked behind a proprietary platform after handover.
You know exactly where your build stands at every stage, from discovery through deployment — no radio silence.
A post-launch support window is built into every engagement, so tuning after real usage starts doesn't become your problem alone.
Fixed-scope pricing to start — final quotes depend on the number of agents and integrations involved. Running a large automation program? Contact us for an Enterprise quote.
$2,500 starting
One agent scoped to a specific business process or task.
$9,000 starting
Multiple coordinated agents handling a larger, multi-step workflow.
Straight answers to what clients usually ask before starting a build.
We build on LangChain, LangGraph, and CrewAI for orchestration, and OpenAI, Anthropic (Claude), Google Gemini, or open-source models like Llama and Qwen depending on the use case and budget.
Both. Simple tasks get a single well-scoped agent. More complex workflows get a coordinated multi-agent system where each agent handles a specific part of the process.
Yes. We build direct API and tool-calling integrations into your CRM, help desk, database, or internal tools — anything that exposes an API.
Agent access to your systems is scoped to what it needs, credentials are managed securely, and we can design around your existing compliance requirements before any development starts.
You do, fully. The architecture, prompts, and integration code are yours — nothing is locked behind a proprietary platform or an ongoing license with us.
Agent development requires specific patterns — orchestration frameworks, RAG pipelines, tool-calling, evaluation — that a generalist dev typically hasn't built before. We build these specifically and have the frameworks and process already in place.
A single-agent build typically takes 2 to 4 weeks. A multi-agent system usually runs 6 to 10 weeks, depending on the number of tools and integrations involved.
Yes. Every engagement includes a post-launch support window, and ongoing support plans are available as usage grows or requirements change.
See the full range of AI agent services we build, from development to voice to system integration.
A common first agent to build — a chat-based agent for your website or support channel.
The integration layer that connects any agent we build into your CRM and internal tools.
Tell us about the workflow and we'll scope an agent build around it.
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