A digital engineering firm that builds your agentic system to order, or a platform already running that its own engineers adapt to you
On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network.
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Nagarro sells digital engineering: teams that design, build, and scale intelligent products, including enterprise-grade AI agents and agentic systems. It is capable engineering delivered as a service, typically at rates well below the largest consultancies.
For organizations that want something genuinely bespoke and have a clear specification, that is a reasonable purchase, and the cost advantage over a tier-one integrator is real.
The structural point is the same one that applies to every services firm, regardless of rate: the engagement begins with an empty repository.
Whatever the day rate, the first months go to retrieval, evaluation, guardrails, access control, audit logging, and model routing — infrastructure that is identical across clients and that a services model necessarily rebuilds each time.
by ibl.ai
Owned platform + forward-deployed engineeringby Nagarro
Digital engineering services firm| Criteria | ibl.ai | Nagarro AI Engineering |
|---|---|---|
| A Working Platform | In production with 1.6M+ users from 400+ organizations, deployed on your infrastructure. | People and method. The platform is built during the engagement. |
| Domain and Industry Depth | Production deployments across higher education, K-12, government, legal, financial services and healthcare, with sector agents already built and running. | Strong, cost-competitive engineering capability with genuine agentic AI experience and a product-engineering culture rather than a pure advisory one. |
| Time to First Production Workload | Weeks — there is no construction phase, because the platform already runs. | Quarters, most of them spent building infrastructure common to every client. |
| Capacity to Deliver Your Programme | A dedicated forward-deployed team plus a finished platform delivers the programme end to end — no bench to wait on and no second firm required. | Very large global staffing, though your programme is resourced from it and priced accordingly. |
| Criteria | ibl.ai | Nagarro AI Engineering |
|---|---|---|
| Source Code Ownership | Full source under a perpetual licence, running on your infrastructure. | A contract question rather than a product property, and frequently under-negotiated. |
| Model Freedom | Model-agnostic by construction — any LLM, switchable without rewriting the platform. | Whatever was built in, and whatever the maintaining team will keep supporting. |
| Independent Operability | Documented, supported, and maintained upstream so your own team can run it. | Depends on knowledge transfer and on continued access to whoever built it. |
| Can It Run Air-Gapped | Yes — the same deployment runs on-premise or fully air-gapped with no outbound connectivity. | Achievable as bespoke scope, but it is significant additional engineering rather than a property of the offering. |
| Criteria | ibl.ai | Nagarro AI Engineering |
|---|---|---|
| How It Is Priced | A flat platform licence plus a bounded integration engagement. | Typically time-and-materials or fixed-price engineering engagements. |
| Is There a Ceiling | Yes — the licence plus the compute you run. Extending to more users does not multiply it. | Bounded by the contract if fixed-price, otherwise by the estimate's accuracy. |
| Cost of Undifferentiated Infrastructure | Zero — retrieval, guardrails, access control and audit already exist and are amortised across every customer. | Funded by you, and rebuilt for the next client afterwards. |
| Ongoing Maintenance | Upstream releases carry model support, protocol updates and security fixes. | A separate contract, or an internal team, for a system built only for you. |
Starting from a finished platform removes most of the hours entirely. The comparison that matters is total cost to a working outcome, not the hourly rate.
A competitive day rate genuinely reduces the cost of the same work — but it is still the same work, including the infrastructure that is not specific to you.
A 30% lower rate on 100% of the work loses to a bounded engagement on the 20% that is actually yours.
A licensed platform is maintained upstream — model support, security fixes, protocol updates arrive in releases you can take or leave, with the source in your possession.
A bespoke system is maintained by whoever will take the contract. Every model release and protocol change becomes a backlog item somebody has to fund.
The build cost is visible and the maintenance cost is not. Over three years the second usually exceeds the first.
On top of a base: your data model, your workflows, your domain logic, your integrations — extended in a fork you own.
A services engagement will happily build all of it, including the parts that already exist elsewhere, because that is what was asked for.
Custom should mean the parts that are yours. When it means the whole stack, the word is doing too much work.
Novel requirements are built faster on top of a working base than from an empty repository, and you hold the source either way. Our engineers build the bespoke part; nobody rebuilds the platform underneath it.
Retrieval, agents, guardrails, and audit are not differentiating. Building them again is spending budget on parity.
A licensed platform receives upstream maintenance. A bespoke system needs a permanent owner, and that cost outlives the build.
The cheapest hour is the one not worked. Removing the construction phase beats negotiating its rate.
Timeline: Four to ten weeks depending on how much has already been built
Timeline: Days to weeks to contract
ibl.ai is the agentic AI platform where you own all the code and the data. You self-host the entire stack inside your own perimeter, run it model-agnostic across any LLM and switch anytime, and pay by usage with no per-seat pricing — so you can deploy anywhere: your cloud, on-premise, GovCloud, or fully air-gapped.
ibl.ai gives an engineering-led organization the base to build on. The platform is in production with users from 400+ organizations and ships with the full source code under a perpetual licence. That means the engineering budget goes to your data model, your workflows, and your integrations rather than to another implementation of retrieval, guardrails, and audit logging. Forward-deployed engineers work alongside your team, and upstream releases keep model and protocol support current without a bespoke maintenance contract. You own all the code and the data, run it model-agnostic across any LLM, with no per-seat pricing, and can deploy anywhere including fully air-gapped.
Full source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform — the stack itself is yours.
Run any LLM — Claude, GPT, Gemini, Llama, Command, or your own fine-tune — and switch providers without rewriting the platform.
Usage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ.
Your cloud, your VPC, on-premise, GovCloud, or a fully air-gapped network with no outbound connectivity.
1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.
ibl.ai is family-owned and operated from New York, NY — a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on.
See how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.