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Turn AI
into systems your business can run on.

We design, deploy, and maintain production-grade AI systems that integrate with existing infrastructure and meet security and scalability requirements.

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Our services

Four ways to move AI into real work.

Start with a working prototype, an engineering team, a manual workflow, or a commercial bottleneck. We build the system around the point where progress currently stops.

01 / From prototype to live system

Turn a working AI prototype into a system people can rely on.

You already have a PoC that proves the idea. We take it through the engineering and rollout work required for real internal or customer-facing use.

The problemWhy the PoC gets stuck

Most prototypes are built for one controlled demo. They assume clean data, a small test group, and an engineer who can fix each failed run. Production adds private data, permissions, integrations, changing inputs, more users, and failures that cannot depend on constant supervision.

The solutionHow we take it live

We rebuild the fragile parts, connect the system to your infrastructure, and add security, evals, monitoring, and recovery. Then we roll it out in stages. Internal teams use it first, and customer access follows once the system has proved it can run safely and reliably.

Built for
Innovation leadersTransformation teamsCTOs and tech leadsProduct owners
  • Rebuild the prototype for production load
  • Connect live data and business systems
  • Add security, evals, monitoring, and recovery
  • Roll out to internal and external users
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An engineering team reviewing an AI system before its production release.
PoC to production / service 01

02 / Safe AI coding at team scale

Help every engineer use AI coding tools without lowering code quality or breaking governance.

We redesign the software delivery process so AI can support architecture, implementation, testing, review, and release. Engineers can use their preferred coding tools, while the company keeps one set of security rules and quality gates.

The problemWhy individual AI use does not scale

Engineers are already trying AI tools, but each person uses them differently. Sensitive context can leave approved systems, generated code is hard to audit, and reviews become inconsistent. Speed goes up locally while security, quality, and ownership become harder to control.

The solutionOne AI SDLC for the whole team

We define how AI can access repositories, create plans, write code, run tests, and prepare releases. Policies and audit trails are built into the workflow. Engineers keep the tools they work best with, but every change follows the same route into production.

Built for
CTOs and VPs of EngineeringEngineering managersPlatform and security teamsLegacy system owners
  • Map one approved workflow from ticket to release
  • Control repository access and model context
  • Automate tests, reviews, and security checks
  • Train teams and measure adoption
AI coding tools

Give the whole engineering team one secure way to plan, build, test, review, and release code with AI.

Legacy modernization factory

Modernize a legacy system with AI-assisted delivery, then use the same governed workflow for every future change.

Explore this service
An engineering team reviewing code, tests, and release checks together.
AI-native engineering / service 02

03 / AI workflow automation

Replace a slow manual workflow with an AI system that completes the work across your existing tools.

We automate a complete business process, including the handoffs and exceptions between steps. The result can be an AI workflow, agent, RAG system, or a combination. The problem decides the architecture.

The problemWhy simple automations stop halfway

Most workflows live across email, documents, CRM, internal tools, and decisions held in people's heads. Automating one task still leaves staff copying data, checking context, and moving work to the next system. The process stays slow because the handoffs remain manual.

The solutionBuild around the complete workflow

We map every step, decision, data source, and exception. Then we build the smallest system that can complete the work safely. It connects to your existing software, asks for human approval when needed, and records what happened so the process can be improved.

Built for
COOs and operations leadersProcess ownersTransformation teamsTechnical sponsors
  • Map the full workflow and its exceptions
  • Select the right AI pattern for the problem
  • Connect data, tools, and approval steps
  • Measure cycle time, cost, and failure rate
System patterns

We use AI workflows, agents, RAG, and knowledge systems only where each pattern has a clear job to do.

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An operations team mapping workflow steps, exceptions, and system handoffs.
AI workflows / service 03

04 / Faster intake and more accurate quotes

Turn every inbound request into a qualified opportunity and every large RFQ into a defensible quote.

We build two commercial systems. An Intake Engine responds while the buyer is still engaged and prepares the first offer. An RFQ System manages the deeper work behind complex RFI, RFP, and high-value quotes.

The problemWhy opportunities are lost or underpriced

New requests wait in inboxes while sales looks for context and decides who should respond. Large RFQs create a different risk. Research, scope, estimates, pricing, and approvals are spread across people and files, so proposals take too long and assumptions are hard to verify.

The solutionGive each request the right workflow

The Intake Engine enriches, qualifies, routes, and drafts a response within minutes. The RFQ System researches the client, finds similar projects, coordinates scope with delivery teams, checks estimates and margin, and builds the final offer. Actual delivery results feed back into future quotes.

Built for
Heads of SalesCommercial operationsEstimating and pre-sales teamsIndustrial and software firms
  • Respond to new inbound requests within minutes
  • Research clients and retrieve similar projects
  • Coordinate scope, estimates, price, and approvals
  • Compare quoted assumptions with actual delivery
Intake engine

For frequent inbound leads where response speed determines whether sales gets the conversation.

RFQ system

For large deals where scope, price, margin, and delivery assumptions need careful review.

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A commercial team reviewing a customer request, technical scope, and cost estimate.
Intake and RFQ / service 04

Success stories

What changed after the system went live.

Three implementations across product, engineering, and sales. The build matters, but the operating result is what the team keeps.

01Cross-industry delivery
PoC to productionSuccess story 01

AI prototypes moved from controlled demos into systems people could rely on.

Across internal and customer-facing AI systems, the same gap appears. A PoC proves that the idea can work. It does not prove the system can handle live data, real integrations, failures, security rules, or people depending on it every day. We build that missing production layer, then roll the system out to internal teams or external users.

  1. 01Audit the PoC and define the production gap
  2. 02Build integrations, evaluations, and observability
  3. 03Add security, failure handling, and system ownership
  4. 04Roll out to internal and external users
Read the full case study
30+PoC-to-production deployments
2-5 weeksTypical path to the first live release
74% → 98.4%Task success rate from pilot to production
02Ecommerce marketplace
AI SDLC for engineering teamsSuccess story 02

One governed AI development workflow for more than 260 engineers.

An ecommerce marketplace had teams using different coding assistants with no shared way to plan architecture, pass context, test generated code, or approve production changes. We designed one secure AI SDLC that works across coding tools and repositories. It connects architecture, implementation, tests, review, and deployment in a single engineering loop.

  1. 01Define approved tools and repository context
  2. 02Turn architecture and tickets into testable plans
  3. 03Enforce tests, reviews, and security checks
  4. 04Measure delivery speed and escaped defects
Read the full case study
260+Engineers using the AI SDLC
~8xFaster ticket-to-production cycle
<3%Tickets returned by QA for defects
1Unified workflow across coding tools
03Building materials wholesale
Agentic intake engineSuccess story 03

Wholesale buyers reached a prepared salesperson before they called another supplier.

A regional building materials wholesaler received stock checks and quote requests by phone, email, and web forms. Each seller had to search several systems for availability, account pricing, delivery options, and substitutes before answering. During busy hours, customers waited long enough to call another supplier. The intake engine collected the request, filled in missing account and product details, checked stock and pricing rules, then prepared the seller to respond with a complete offer. Median first response fell to 2 minutes 18 seconds, and revenue from inbound requests grew by 18%.

  1. 01Capture and enrich the inbound request
  2. 02Qualify the job and collect missing details
  3. 03Prepare scope, pricing, and offer options
  4. 04Route the seller with a complete opportunity
Read the full case study
2m 18sMedian first response time
+31%Qualified lead-to-opportunity rate
+18%Revenue from the inbound channel
64%Quotes ready before seller review

About us

Built with people who use AI as the core of their work, every day.

Harnessloop is led by Bartosz Ludera, with more than five years of experience building companies and improving how they work. Over the past three years as an AI consultant, he has helped dozens of companies turn AI into useful operating systems.

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

Answers before the first call.

How is a project priced?

Every project uses outcome-based pricing. You agree a fixed fee for the defined system and pay after delivery. There are no time-and-materials contracts or hourly billing.

How long does a project take?

Most projects take 2-4 weeks. Our AI-native operating model compresses work that often takes six months into a few focused weeks, without losing the controls needed for production.

Do we need to replace our existing systems?

No. Systems of record stay in place. The work is to connect the right context, actions, approvals, and audit trail around them.

Which systems can you connect to?

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.

Will we own the system?

Yes. Every system is custom-built for your company and transferred to you exclusively. Your team owns the implementation, the workflows, and the result.

The LangChain stack in practice

Frameworks give the system a structure. They do not replace the operating work.

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.

LangChain Ambassador

Framework expertise, closer to the source.

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.

01

Earlier context

Direction before it becomes a delivery concern.

02

Practical knowledge

Guidance from real implementation work.

03

Direct route

Access to the people building the framework.

LangChain in production

Used by teams at

LyftRipplingKlarnaMercorVantaGitLabZIPElasticRakutenClay

Why Harnessloop

Built as an AI-native company, not a conventional agency with AI added on.

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

AI moves the work. People own the call.

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.

  1. 01Observe the real work
  2. 02Run the repeatable work through AI
  3. 03Review decisions with the people accountable
  4. 04Feed outcomes into the next release
A circular operating loop connecting human judgment, AI execution, and operational feedback.
Human judgmentAI executionLive feedback
01

AI-native company

The delivery team works the way the system will work.

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.

02

2-4 week increments

A working increment before a long programme.

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.

03

Forward-deployed engineering

The build happens close to the operation.

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.

04

LangChain Ambassadors

Framework expertise when the technical stakes rise.

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

How we build a company with AI from the ground up.

A useful perspective from Y Combinator on the operating model behind an AI-native company.

Watch on YouTube

Start with the work

Bring the workflow that needs attention.

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

Book a 20-minute consultation.

Bring the workflow that feels slow or fragile. We will determine whether it is a sensible candidate for an AI system.

Bartosz LuderaBartosz LuderaFounder, Harnessloop

Choose a time for a 20-minute consultation.

Send a workflow brief

Prefer to write it down?

Tell us where work waits, repeats, or falls through the cracks.