We design, deploy, and maintain production-grade AI systems that integrate with existing infrastructure and meet security and scalability requirements.
Official LangChain Ambassador
LlamaIndex Deployment Partner
Who we support
We help 100-500 person companies turn a useful proof of concept into an operating system that can be owned, secured, and improved.
01 / PoC to production
Integrations, controls, ownership, and evaluation are still missing.
What this means
The business case is proven, but the prototype still depends on manual supervision or isolated data. We turn it into a system with access rules, clear ownership, measurement, and a reliable route into daily work.
02 / Stalled AI effort
We reset the work around one costly workflow and a measurable outcome.
What this means
The team has seen promising experiments, yet delivery stopped around unclear scope, disconnected teams, weak data, or no operating owner. We identify the bottleneck and rebuild the case around one workflow people need to use.
03 / Clear need, no route
We create the system design and carry it into live operation.
What this means
Leadership knows where AI could remove costly work, but lacks the product, architecture, and delivery capacity to make it safe. We own the path from workflow evidence to a live, maintainable system.
Delivery loop
One continuous loop. Each stage leaves evidence for the next one, while feedback from production improves the next decision.
01 / Workflow discovery
Trace people, systems, decisions, exceptions, and cost.
02 / System blueprint
Set architecture, decision boundaries, ownership, and proof.
03 / Build & integrate
Ship agents, integrations, controls, interface, and evaluation.
04 / Production release
Introduce access controls, monitoring, fallbacks, and review.
05 / Improvement loop
Review exceptions against KPI and feed learning into the next loop.
Success stories
A selection of delivery patterns from work in service, commercial, and operational teams. Each system starts with a specific owner, decision path, and measure.
A secure intake and knowledge system for customer reports, outage signals, and live service questions.
A system for a marketing services team that turns a vague request into a priced, usable response.
A fast first response for inbound leads, with qualification and clear routing for the commercial team.
What we build
Every engagement is shaped around the work people need to complete, the systems they already rely on, and a clear route into daily operation.
Connect repeatable handoffs across the systems your team already runs.
Give teams a controlled way to act on information, tools, and exceptions.
Make internal context usable with sources and clear review points.
Coordinate workflows that need data, decisions, and human ownership.
About us
Harnessloop is led by Bartosz Ludera. We work with operating teams from the first workflow map through release and the improvements that follow.
Our delivery approach is shaped by production work, not innovation theatre. The system needs a visible owner, clear boundaries, and evidence that it is helping the work move.
Before we start
It has a clear owner, repeats often enough to matter, and creates an obvious cost when it waits or goes wrong. We map those conditions before we commit to a build.
No. Systems of record stay in place. The work is to connect the right context, actions, approvals, and audit trail around them.
The decision point is defined before implementation. The system can prepare, retrieve, route, and monitor. Your team keeps the decisions that need authority or judgment.
A mapped workflow, the first production-ready path, agreed controls, and evidence from the people who will use it. The scope is small enough to judge, not a promise to solve everything at once.
We work with the systems that already hold the work: CRMs, helpdesks, internal knowledge, document stores, operational tools, and custom APIs. The first step is confirming access and boundaries.
We define the access model, data boundaries, logging, and approval steps before implementation. The system only receives the context it needs for its part of the work.
We observe real exceptions, review traces and outcomes, then decide what improves next. The system keeps changing with the work instead of being handed over and forgotten.
The LangChain stack in practice
We use the stack where it makes an AI system easier to inspect, change, and run with confidence.
LangChainConnects models, tools, data, and the actions an agent can take.
LangGraphMakes workflow state, approvals, and exception paths explicit.
LangSmithShows traces and evaluations so the team can improve what happens in production.
Official LangChain Ambassador
We stay close to the people and releases behind the framework. Clients get earlier product context, practical implementation knowledge, and a direct route to the founding team when an engagement calls for it.
Direction before it becomes a delivery concern.
Guidance from real implementation work.
Access to the people building the framework.
LangChain in production
Why Harnessloop
Our operating model is part of what you buy. It changes how quickly the work gets real, how close it stays to your team, and what you can judge before committing further.
The operating loop
Every delivery cycle connects operational input, AI execution, human judgment, and live feedback. That is how an AI system becomes part of a company instead of a separate experiment.
AI-native company
AI handles research, drafting, testing, and observation inside the delivery loop. People keep the decisions that need judgment or accountability. You get progress without losing control.
2-4 week increments
We define a slice small enough to ship and judge in two to four weeks, rather than disappearing into a three to six month build. You see evidence early and choose the next step with it.
Forward-deployed engineering
We work alongside users, source systems, and real exceptions. You get something shaped by live work, not a polished demo that needs to be rebuilt after handover.
LangChain Ambassadors
Our LangChain ambassador relationship keeps the implementation current and gives us a direct route to framework experts. You get fewer blind spots in architecture and delivery.
See the model in context
A useful perspective from Y Combinator on the operating model behind an AI-native company.
Watch on YouTubeStart with the work
Pick a time for a working session or send a short brief. Either way, we will come prepared to understand where the work gets stuck.
Talk through the work
Bring the workflow that feels slow or fragile. We will determine whether it is a sensible candidate for an AI system.
Bartosz LuderaFounder, HarnessloopSend a workflow brief
Tell us where work waits, repeats, or falls through the cracks.
