The first thing our clients trust us with is the decision to trust us. They engage Adorbis because they need AI capability on data too sensitive, too proprietary, or too regulated to hand to public APIs. That's also why you won't find their names on this page.
Our non-disclosure policy
Every Adorbis engagement begins with a mutual non-disclosure agreement. Not an afterthought. Not page 47 of a master services contract. An NDA is the first document we sign, before a single line of code is reviewed, before scope is discussed in detail, and before we ever ask to see production data.
The scope of what we treat as confidential includes:
- Your identity as a client. We don't publicly acknowledge that we work with you. No logos on our site. No case studies with identifying details. No social media posts about the project.
- Your business data. Everything you share — code, databases, customer records, financial data, strategic plans — stays within the engagement perimeter. Encrypted at rest and in transit. Destroyed within 30 days of engagement close unless retention is legally required.
- Your processes. The internal workflows, decision criteria, and organizational nuances we learn while building for you are not reused, written up, or shared.
- Your systems. Architecture, integrations, credentials, and access paths remain confidential forever, even after the engagement ends.
This is not a marketing posture. It's the operating condition of our business. Clients come to Adorbis specifically because their other options — public APIs, big consultancies, in-house teams they haven't hired yet — don't meet their data-handling threshold. Breaking confidentiality, even subtly, would end the firm.
How we demonstrate competence without naming clients
We know the reasonable question: "If you can't show me who you've worked with, how do I know you can do the work?" Here's how we answer that — six concrete signals that require no violation of any NDA:
Engagement patterns
We describe the shape of engagements we've delivered (industry, scale, technology stack, outcome) without naming the client. Each pattern has been delivered to paying clients under contract.
Technical specificity
Our capabilities pages reference the specific tools, models, and architectures we actually use in production — Ollama, vLLM, n8n, Qdrant, Firebase, Llama, Mistral, DeepSeek. Specificity only comes from doing the work.
Reference-on-request
After mutual NDA, we arrange 1:1 reference calls between you and 2–3 current clients in your industry. You ask them anything. We're not on the call.
Paid pilot first
For larger engagements, we propose a 2–4 week paid pilot with a clear success metric. If we don't hit the metric, we don't continue to phase 2 and you've lost a small fraction of a full engagement's cost.
Government recognition
Adorbis Technology Pvt Ltd is a DPIIT-recognized startup under the Startup India initiative, Govt of India. A verifiable credential that is harder to obtain than most agency listings.
Six years of operation
Adorbis has been shipping production software for paying clients since 2019 — predating the current AI hype cycle. We've survived multiple market conditions without pivoting our delivery model.
Representative engagement patterns
The following are illustrative of the shapes of engagements we deliver — not descriptions of any specific client project. Names, industries, and details have been genericized. A prospect evaluating us for similar work can ask for reference conversations with actual clients who match these patterns.
Pattern A · Private LLM deployment
A regulated-industry client needs GPT-class language capability but cannot send data to public APIs due to compliance constraints. Adorbis deploys a self-hosted LLM stack on the client's infrastructure — model serving, chat interface, retrieval-augmented generation over internal documents, SSO integration, audit logging. Handover includes full infrastructure-as-code, runbooks, and a 30-day support window. Typical timeline: 6–10 weeks.
Pattern B · Multi-agent automation pipeline
A client with high-volume document or communication workflows needs orchestrated AI pipelines to replace manual processing. Adorbis designs an n8n or LangGraph-based system with specialized agents for each step (extraction, classification, routing, approval, notification), integrates with the client's existing CRM/ERP/inbox, and runs on their private infrastructure. Typical outcome: 60–90% reduction in manual touch time. Timeline: 4–8 weeks.
Pattern C · Domain-specialized fine-tuned model
A client has proprietary domain data (legal, medical, financial, technical) and needs a language model that understands their terminology better than general-purpose models. Adorbis fine-tunes an open-weight model (Llama, Mistral, or Qwen) on the client's corpus, builds an evaluation harness proving the fine-tune outperforms GPT-4/Claude on the client's specific task, and deploys the result. The client fully owns the resulting model weights. Timeline: 3–6 weeks.
Pattern D · AI-native product infrastructure
A technology company building an AI-powered product needs deep engineering partnership — not just advisory but hands-on architecture, integrations, and production deployment. Adorbis embeds with the client's engineering team for 3–6 months, builds the AI-specific components (model selection, orchestration, state management, failsafes, observability), and hands over a production-operating system with full documentation. Engagement shape: embedded team.
Our engagement framework
Every new client engagement follows the same sequence:
- 1. Email + discovery call. You email info@adorbistech.com with a short description. Within 24 hours we reply. If there's mutual fit, we schedule a 30-minute call.
- 2. Mutual NDA. Before we discuss your systems or data in detail, we execute a mutual NDA. Your standard template or ours — whichever is faster.
- 3. Reference conversations. On request, we arrange 1:1 reference calls with 2–3 current clients matching your industry, scale, or use case. Typically scheduled within 7 days of NDA execution.
- 4. Scoped proposal. We deliver a written proposal with fixed scope, fixed price, fixed timeline, and defined success criteria. No hourly billing.
- 5. Paid pilot or full engagement. For smaller scopes we go direct to full engagement. For larger scopes we propose a 2–4 week paid pilot with a kill-switch before phase 2.
- 6. Delivery, handover, NDA continues. The NDA survives the engagement. We do not use your data, your name, or your architecture in marketing after we're done.
Industries we've shipped into
Across six years of operation we've delivered into a range of industries including financial services, healthcare, retail and eCommerce, manufacturing, media, legal tech, and education. We do not publish specific combinations of industry + engagement type that would identify individual clients. If you're in any of these sectors and want to know if we have directly-relevant experience, ask on the first call — we'll give you a clear yes or no.