AI Agent Development
AI Agent Development Company
Your competitors are not hiring more staff — they are deploying agents. ISEMI ships production AI agents: we scope the job your agent must do, build it on the right model stack, wire it into your systems, and keep it reliable long after launch. We know it works because we run our own AI products on the same discipline. Outsource the hard part to engineers who have already shipped it. Whether you need a single support agent or a coordinated multi-agent system, you get a team that has taken agents from prototype to production and kept them running — not a lab that stops at the demo.
Custom AI Agents, Scoped to a Job
We start from the task, not the tech: support triage, lead qualification, content operations, data entry — then engineer an agent that completes it with measurable results.
Multi-Agent System Architecture
When one agent is not enough, we design coordinated ecosystems — specialized agents for research, planning, and execution that hand work to each other reliably.
LLM Integration & Tool Use
Function calling, retrieval over your private data, and integrations with the systems you already run — your agent acts inside your stack, not beside it.
Conversational & Voice Experiences
Multilingual natural-language interfaces, from chat assistants to speech-driven flows like the pronunciation coaching we built into Spikdi.
Evaluation & Production Operations
Prompt regression tests, guardrails, monitoring, and cost controls — so the agent that impressed in the demo still performs in month six.
From discovery sprint to a production agent
We do not disappear for months and return with a black box. An ISEMI agent build moves in short, visible stages, so you see working software early and steer it the whole way.
Discovery sprint
We map the exact task your agent must own, agree on the metrics that define success, and prototype against your real data. You leave the sprint with a working proof of concept, a target architecture, and a fixed-scope estimate — not a slide deck.
Build
We engineer the agent on the model stack the discovery proved out: tool integrations into your systems, retrieval over your private data, guardrails, and an evaluation harness that scores every change. You review in weekly increments and steer as the behaviour takes shape.
Operate
Shipping is the start, not the finish. We monitor quality and cost in production, run prompt-regression tests before every change, and tune the agent as your edge cases and volumes grow — the same discipline we apply to our own AI products.
Ways to work with us
Choose the commitment that matches where you are. Every model delivers into your repositories and your cloud, and every one can start with a discovery sprint.
Discovery sprint
A one-to-two week fixed-price engagement to de-risk the idea: we prototype the agent against your data and hand back a proof of concept, an architecture, and a scoped plan. Many clients start here before committing to a full build.
Fixed-scope build
A defined agent, a defined timeline, and a price agreed up front. Best when discovery has settled what to build and you want a predictable delivery with clear acceptance criteria.
Monthly team
An embedded ISEMI squad that iterates with you month to month — ideal when the agent will keep growing, integrating new systems and absorbing new tasks over time. Scale the team up or down as the roadmap changes.
The stack we build on
We are model- and framework-agnostic by design, choosing the right tool per task instead of forcing every problem through one library. A typical agent engagement draws on:
Agent frameworks
Google's Agent Development Kit (ADK), plus LangGraph- and CrewAI-class orchestration when a graph of specialised agents fits the problem better than a single loop. We keep the orchestration layer thin so your team can maintain it.
Tools & the Model Context Protocol
Agents earn their keep by acting, not chatting. We wire function-calling and MCP tool servers so the agent can read and write in the systems you already run — issue trackers, wikis, databases, cloud storage, and internal APIs.
Retrieval over your data
Grounded answers come from your knowledge, not the model's guesswork. We build retrieval pipelines over your documents and records with the chunking, embedding, and re-ranking each corpus needs.
Model choice
Gemini, Claude, GPT, and open-weight models, selected per step for quality, latency, and cost. Because the stack is not welded to one provider, we can swap models as better or cheaper ones ship.
Evaluation & guardrails
Every agent ships with an eval suite: prompt-regression tests, scored task runs, output validation, and cost and latency budgets — so quality is a number we watch, not a vibe we hope for.
Spikdi
Our flagship proof for agent work: Spikdi orchestrates Gemini, Whisper, and Imagen behind one product — an agent pipeline that generates lessons, listens to pronunciation, and narrates back, serving real learners in production.
View ProjectSee it in production
The best proof that we ship agents is the agents we already run. Read how these were built:
Spikdi
An agent pipeline that orchestrates Gemini, Whisper, and Imagen behind one language-learning app — lesson generation, live pronunciation coaching, and narration, running in production.
Read the case studyAI Agent Jira Bot
A Google Chat agent on Agent ADK with MCP tools that reads and writes across Jira, GitLab, Notion, Confluence, and more to run a team's sprint board from plain-language requests.
Read the case studyFlow Kit
An open-source agent that drives an entire media pipeline from a single command — proof of how we make agents orchestrate real, multi-step work end to end.
Read the case studyFAQ
How does an engagement with ISEMI work?
Most agent projects start with a one-to-two week discovery sprint: we map the task, define success metrics, and prototype against your real data. From there you choose fixed-scope delivery or a monthly team that iterates with you.
Why work with ISEMI on AI agents?
We build and operate our own AI products — Spikdi, Flow Kit, Flowboard, Postforge — so the AI layer we ship for you rests on systems we run in production ourselves. You get that shipping discipline with distributed coverage across US, EU, and APAC working hours.
Which models and frameworks do you work with?
We are model-agnostic: Gemini, Claude, GPT, and open-weight models, chosen per task for quality, latency, and cost. We avoid framework lock-in so you own a system your team can maintain.
Who owns the code and the data?
You do. Agents are delivered into your repositories and your cloud accounts; your data stays in your infrastructure, and we sign NDAs before discovery when needed.
How long does it take to ship a production AI agent?
Most first agents reach a working production release within a few weeks of the discovery sprint, depending on how many systems they integrate and how strict the accuracy bar is. The discovery sprint gives you a concrete timeline before you commit to the build, so there are no open-ended estimates.
How do you keep an AI agent accurate and safe in production?
Guardrails, retrieval grounding, and human-in-the-loop approval for high-stakes actions, backed by an evaluation suite that scores the agent on real tasks. We run prompt-regression tests before every change and monitor quality and cost after release, so regressions surface before your users do.