AI agent development services that run in production
We build AI agents that plan their own steps, call your tools, and finish real work. Pack Assist qualifies packaging leads. Farmin reads satellite images. Content Compass analyzes text, images, and video. Each one runs in production with a person in the loop and an eval set that runs before every release.
What is agentic AI engineering?
Agentic AI engineering is the practice of building AI systems that plan their own steps, call tools and APIs, check their own output, and finish multi-step tasks with a person reviewing the risky parts. A chatbot answers one prompt. An agent decides what to do next, runs that step, and keeps going until the job is done or it needs help.
- Agents that qualify leads, answer from your data, and read images.
- Built with Claude, OpenAI, LangGraph, and FastAPI.
- Tool servers with typed inputs, so an agent can only do what you allow.
- An eval set runs before every release. A person reviews anything the agent is unsure about.
- Proven in Pack Assist, Farmin, and Content Compass.
What does an AI agent in production look like in 2026?
In 2026 the model is not the hard part. Tool use, evals, and guardrails are. Every agent we ship gets a tool server in the MCP style, with typed inputs and a short list of allowed actions. It gets an eval set that runs before each release, so a prompt change cannot quietly break a workflow. And it gets a human in the loop for anything that touches money, customers, or deletes data.
We pick the cheapest model that passes the evals, and we log every tool call so you can see what the agent did and why. That is the difference between a demo and a system your team trusts on a Monday morning.
Outcomes
What do you get?
Cost-aware agents
We pick the cheapest model that passes the evals, as we did with Pack Assist.
RAG and fact checks
Agents answer from your data and cite the source. No made-up answers.
Tool and API access
Agents connect to your APIs, databases, and business tools to do real work.
Human in the loop
Dashboards and escalation paths for when an agent needs a person.
Deliverables
What is included?
Sales and qualification agents
Chatbots that qualify leads, answer questions, and hand off to a person. Like Pack Assist.
RAG and knowledge systems
Document Q&A and semantic search with Pinecone, pgvector, or custom vector stores.
Computer vision
Object detection with YOLO and satellite imagery, as in Farmin.
Multimodal analysis
Text, image, video, and document analysis with OpenAI and Twelve Labs.
Tool servers and guardrails
MCP-style tool servers with typed inputs, allow lists, and logs of every call.
Evals and review dashboards
Scored test sets that run in CI, plus a dashboard where a person approves the edge cases.
Who we serve
Who is this for?
SaaS Companies
AI agents shipped inside your product so you keep pace with funded competitors.
See the SaaS Companies page
Digital Marketing Agencies
Agents that draft reports and qualify inbound leads for your account managers.
See the Digital Marketing Agencies page
E-commerce and DTC Brands
Agents that answer product questions and chase abandoned carts.
See the E-commerce and DTC Brands page
Software Founders
An agent feature built into your MVP without hiring an ML team.
See the Software Founders page
Why us
Why teams pick TechEmulsion
30+ AI products shipped since 2023. Top Rated Plus on Upwork. US LLC in Wyoming. Here is what that gets you:
30+ AI products shipped
Our engineers have shipped 30+ AI products to production. You get code that holds up under real users and is easy to maintain.
Built for your business
We build around how your team already works. No generic templates, no forcing you onto new software.
4 to 8 week ship cycles
Most builds go live in 4 to 8 weeks, not 6 months. You see working software early and often.
Simple to use
Your team should not need training to use what we build. We design every screen and workflow to be obvious.
NDA and security
We sign an NDA before we see anything. Your data stays yours, and we follow security best practices on every build.
Support after launch
We do not disappear after delivery. Ongoing engagements include monitoring, fixes, and updates.
How does the engagement run?
Most agent projects start as a fixed-scope build for the first workflow, 4 to 8 weeks, then a monthly retainer for evals, model updates, and new tools.
Not a fit if you want a demo for a slide deck, or if the process you want to automate changes every week and nobody owns it.
01.
Discovery
A free 30 minute call. We listen first. No pitch deck.
02.
Plan
We scope the first system that pays for itself and give you a fixed timeline.
03.
Ship and iterate
We build, test on real data, launch in 4 to 8 weeks, then improve it with you.
Questions about Agentic AI Engineering
Answers from the engineers who do the work. Anything else, ask on a free 30 minute call.
By industry
Where this shows up by industry
SaaS Companies
AI features for funded SaaS
In-product agents and chatbots shipped in weeks, on a paid proof of concept first.
Open page
Software Founders
AI engineering team for product founders
From a Lovable prototype to a production agent without hiring an ML team.
Open page
Free tool
MVP scope calculator
Size the first agent build before the discovery call.
Open page
Software Founders & Builders
RAG Chatbot & Knowledge System
Ship a chatbot that answers from your data, not the internet
See the offer
Software Founders & Builders
Agentic Workflows & Automation
Replace manual multi-step work with AI agents that take action
See the offer
All industries
Every industry we serve
Marketing agencies, software founders, ecommerce brands, SaaS companies, and home services. One page per industry.
Browse industries