Private AI that runs offline, on your premises or in your country
Webify.AI builds AI agents that run air-gapped, on your own servers or in an in-country cloud tenancy. We build, hand over and hold none of your data. We are an AI and software company headquartered in Ahmedabad, India, and an IBM Partner.
A free 30-minute call
Ask whether your use case can run offline. You get a straight answer and a rough hardware sizing.
Fixed-price pilots from $12,000
One workflow on your own documents, with success criteria agreed in writing before the build.
Your data stays with you
We hold none of your data and keep no standing remote access. Support runs through sessions you start.
What is private AI, and how do air-gapped, on-premise and in-country cloud differ?
Private AI is a language model and its data running on infrastructure you control, not on a shared AI service. Air-gapped means no network link to the outside at all. On-premise means your own servers, under your normal network controls. In-country cloud means a dedicated tenancy with a provider inside your country.
The engineering is the same in all three: an open-weight model, retrieval over your own documents, guardrails and evaluation. What changes is where it runs and who can reach it. Choose the location your rules and risks require, not the strictest one by default.
Swipe the table sideways to see every column.
| Air-gapped | On-premise | In-country cloud | |
|---|---|---|---|
| Where the model runs | Servers in an isolated network you own | Servers in your own data centre or office | A dedicated tenancy in a cloud region inside your country |
| Internet connection | None. Updates arrive on approved media. | Yes, under your firewall rules. The AI itself makes no outside calls. | Yes, through the provider's network inside the country. |
| Who runs the hardware | You | You | The cloud provider |
| Best fit | Classified or national-security data, critical infrastructure and strict client confidentiality | Banks, insurers, hospitals and law firms that prefer their own hardware | Residency rules that require in-country storage, without buying servers |
| Trade-offs | Slower updates. All support happens on site or through sessions you start. | You buy, house and maintain the GPUs. | You rely on the provider. Check where its support staff and backups sit. |
| Hardware cost | Bought in your name | Bought in your name | Rented by the hour or month |
Offline is the strictest version, and the easiest to prove. Many residency rules can be met by in-country hosting, as the region notes below show. A cloud region inside your country often satisfies them without new hardware.
Private AI is not always the right answer. The UK government's AI Playbook, for example, calls running models locally "not recommended for most production services" (GOV.UK). If your only concern is general privacy, an enterprise AI service with no-training terms may be enough. We will say so on the free call.
A middle path also exists. In a local-first design, sensitive work stays on your servers. Redacted text goes to a hosted model only where your policy allows it.
Who needs private AI rather than a hosted AI service?
Organisations with a hard reason to keep data in place. Typical reasons are client privilege, bank secrecy, patient records, classified data, a data-residency law or a regulator asking for an exit plan. These buyers accept a smaller model and more upkeep in return for control over where data goes.
- Law firms whose engagement terms or professional rules limit where client documents may go.
- Banks, insurers and lenders handling KYC files, credit decisions and bank-secrecy data.
- Hospitals and clinics working with patient records.
- Government bodies and their contractors handling classified or national-security data.
- Energy, oil and gas, and other critical-infrastructure operators.
- Any firm under a data-residency rule that its current AI provider cannot meet.
Demand is narrow but real. In Deloitte's 2026 survey of 3,235 leaders, 83% rated data residency and in-country compute at least moderately important (Deloitte). In a survey for Cloudian, an on-premise storage vendor, 58% of IT decision-makers said residency concerns had delayed or scaled back AI projects (Cloudian).
Large banks are already doing this. HSBC signed a multi-year agreement to run Mistral's models as self-hosted systems inside its own technology environment (HSBC).
Mid-sized firms with only a general worry about data leaks are often well served by enterprise AI services with no-training terms. Private AI earns its cost when a rule, contract or regulator says the data must stay put.
Which AI agents run well offline?
Three agents have the clearest evidence for offline use. They are a private knowledge assistant, document extraction, and Arabic-English drafting and translation. Each does narrow work that a person can check. Contract clause extraction and clinical note drafting run as pilots only. For air-gapped coding help, buy a specialist product and have us integrate it.
Private knowledge assistant
Answers staff questions from your policies, SOPs, HR handbooks and contracts, and shows the passage behind each answer. It suits HR, compliance, operations and legal teams. A typical setup is a 30B-class model on one 80-96GB GPU.
Document extraction
Turns forms, invoices, KYC and lending files into structured data for your systems. A person checks the exceptions. Results are easy to score against known answers, so accuracy is measurable from the first week.
Arabic-English drafting and translation
Drafts and translates letters, summaries and replies between Arabic and English, using Gulf-built models such as Jais 2, ALLaM and Fanar. A person reviews each draft before anything is sent.
Offline-ready Bot Store agents
Several agents in our Bot Store can be deployed the same way, from HR operations to field service.
→ Read more
Each lead agent has public demand behind it. HSBC lists document-heavy lending analysis and translation among the uses of its self-hosted models (HSBC). Document extraction can start small: a compact document model plus an 8-32B language model, on CPU or one GPU. Arabic drafting runs a 7-9B model on one GPU, or a 70B model on two 80GB GPUs.
Two more agents are offered as scoped pilots only, because their results need careful validation:
- Contract clause extraction. It finds clauses, dates and obligations, and flags departures from your standard terms. It extracts; it does not give legal advice or strategy.
- Clinical note drafting. It drafts notes for a clinician to edit. A JAMA Network Open study tested an on-premise, fine-tuned Llama 3-based model at one Korean health system. Documentation time fell by more than 50% when clinicians edited its drafts. Clinical validation and liability make this a supervised pilot, never a shelf product.
Air-gapped code assistants have the strongest demand of all, but specialist vendors already sell them. Tabnine and Mistral Code both document air-gapped deployment. We recommend buying one of these and having us integrate it with your repositories, identity provider and network rules.
Agents that depend on live internet services, such as web search, stay off the offline list. See the offline-ready agents in our Bot Store for the agents we deploy this way. The full catalogue is in the Bot Store, and our wider agent work is on AI agents.
Which models do we use for private AI?
Our default is IBM Granite 4 for English work, with Gulf-built Jais 2, ALLaM and Fanar for Arabic. We add gpt-oss, Gemma or Mistral where they fit the task. Each is released under Apache 2.0, and we check the exact checkpoint's terms before any contract. They run on vLLM, with pgvector for retrieval and Langfuse for monitoring.
Swipe the table sideways to see every column.
| Model | Licence | What we use it for | Hardware |
|---|---|---|---|
| IBM Granite 4 H-Small | Apache 2.0, with cryptographically signed checkpoints | Default for knowledge assistants, extraction and English drafting | One RTX PRO 6000 or one H100, per IBM's documentation |
| Jais 2 (8B and 70B) | Apache 2.0, gated download | Arabic drafting and translation; the 70B for heavy Arabic work | 70B across two GPUs, per its model card; 8B fits one GPU |
| ALLaM-7B | Apache 2.0 | Arabic tasks, especially in Saudi Arabia | One GPU (model card) |
| Fanar-1-9B | Apache 2.0, built on Google's Gemma 2 | Arabic tasks, especially in Qatar | One GPU (model card) |
| gpt-oss-120b and gpt-oss-20b | Apache 2.0 | General reasoning where a person checks the output | 120b fits one 80GB GPU; 20b fits in 16GB (model card) |
| Gemma 4 | Apache 2.0 since April 2026 | Smaller and edge deployments | 31B fits one 80GB H100 (The Decoder) |
| Mistral 3 (3B to 14B dense) | Apache 2.0 | Smaller GPUs and narrow tasks | Targets GPUs down to the RTX 4000 Ada (Let's Data Science) |
Licences as published on each model card or announcement. Fanar's Gemma lineage means we also check Gemma's terms.
Granite 4 fits our clients unusually well. IBM describes it as "the first open model family to earn ISO 42001 certification" (IBM). That certification is IBM's, not Webify.AI's. Granite also comes from the vendor we work with as an IBM Partner.
We choose models on evidence for the task, not on general leaderboards. Vectara's hallucination leaderboard measures how often a model adds unsupported facts when summarising a short document. Lower is better. In its September 2026 update, Granite-4.0-H-Small scored 5.2% and Llama-3.3-70B 4.1%. gpt-oss-120b scored 14.2%, against 9.3% for GPT-5.5 and 12.0% for Claude Opus 4.7. That is why gpt-oss is not our default for answers drawn from documents.
Arabic needs its own evidence. On the QIMMA Arabic leaderboard, built by TII in Abu Dhabi, Jais-2-70B ranks first at 65.81. Among small models, Jais-2-8B scores 57.89, Fanar-1-9B 56.78 and ALLaM-7B 56.51. gpt-oss-20b ranks last at 32.10.
Benchmarks disagree, though. Stanford's HELM Arabic found that older Arabic-specific models lagged, and that the best open model was Qwen3-235B. It did not test Jais 2 or Fanar. The Gulf models also have short context windows: 4,096 tokens for ALLaM-7B and Fanar-1-9B, and 8,192 for Jais 2. So we test Arabic on your own documents before choosing a model.
For regulated buyers in the US, UK, EU and Australia, our default list has no Chinese-origin models. Menlo Ventures found enterprises reluctant to use them (Menlo Ventures).
Open models trail the best hosted ones, but less so on narrow tasks. Epoch AI estimates the best open-weight models are about four months behind (Epoch AI). Models small enough for one consumer GPU trailed by 6-12 months in 2025 (Epoch AI). Grounding answers in your own documents narrows that gap.
- Serving: vLLM on servers, and Ollama or llama.cpp on laptops and demo boxes.
- Retrieval: PostgreSQL with pgvector, which suits IT teams that already run Postgres.
- Monitoring: self-hosted Langfuse, whose documentation states that "Internet access is optional".
- Guardrails: Granite Guardian checks groundedness and harmful content. It is English-only, so Arabic deployments get a separate check.
- Vendor support, if you want it: IBM watsonx, or NVIDIA AI Enterprise at $4,500 per GPU per year list (NVIDIA). These are options, not our default.
What does private AI cost?
The first call is free. A 3-5 day discovery starts at $3,000, and a 2-4 week fixed-price pilot at $12,000. Production installs are quoted per component. A managed retainer starts at $1,500 a month. Hardware is bought in your name at cost; we do not resell GPUs.
| Step | What you get | Price (USD) |
|---|---|---|
| "Can this run offline?" call | 30 minutes, a straight answer on your use case and a rough hardware sizing | Free |
| Offline AI discovery, 3-5 days | One to three use cases reviewed, a data and hardware check, a model shortlist, a model-licence register and written success criteria | From $3,000 |
| Fixed-price pilot, 2-4 weeks | One workflow on your own documents, on your hardware or a rented GPU, with role-based access, an audit log and an evaluation report | From $12,000 |
| Production install | The air-gap kit, integrations with your systems, an optional high-availability setup, documentation and handover | Quoted per component |
| Managed retainer | Patching, model refresh, re-evaluation and support through sessions you start | From $1,500 a month |
| Hardware | Bought in your name. We help you size and specify it. | At cost; we do not resell GPUs |
Prices are starting points. Each paid step is quoted in writing before work begins.
Published prices from other private-AI integrators give a reference point. One US firm lists a $3,500+ readiness audit, $18,000-$60,000 implementation sprints and managed services from $1,500 a month (Main and Machine). A UK G-Cloud 15 listing charges £2,500 setup plus £500 a month per hosted model container (Viewdeck).
Is private AI cheaper than a hosted API? Usually not. Hosted versions of the same open models are now very cheap. Artificial Analysis lists gpt-oss-120b at $0.04 to $0.39 per million tokens across providers (Artificial Analysis). Lenovo's best case for on-premise inference is about $0.13 to $0.16 per million tokens (Lenovo Press). That assumes full load around the clock and leaves out staff costs.
An academic break-even study reaches a similar view. It finds self-hosting pays mainly above about 50 million tokens a month, or where residency rules require it (arXiv 2509.18101). That study also excludes staff costs. Meanwhile, NVIDIA listed its RTX PRO 6000 at $16,000 in August 2026, up from $13,250 in June (Let's Data Science).
So we do not sell private AI as a way to save money. Buyers choose it for other reasons:
- A rule, contract or regulator requires the data to stay in place.
- No outside provider sees your prompts, documents or answers.
- The system keeps working with no internet connection.
- The bill is fixed: hardware and support, with no per-token charges.
- You keep a model you control if a provider changes its terms or prices.
The one honest cost case is narrow: a small model running one high-volume task on a single GPU.
What hardware does private AI need?
Less than many buyers expect for a pilot. A demo box proves the idea for a few users. A departmental server with two 96GB RTX PRO 6000 GPUs lists at about $68,000 from Lenovo. Enterprise servers with four to eight GPUs list at about $113,000 to $398,000 (Lenovo Press).
Swipe the table sideways to see every column.
| Tier | Example | What it is for | Price |
|---|---|---|---|
| Demo box | A workstation with one RTX 5090 (32GB), a Mac Studio, or an NVIDIA DGX Spark (128GB) | A few users, small models, and proof that the system runs offline. The DGX Spark decoded a 70B model at only 2.7 tokens a second in LMSYS tests. | RTX 5090: under $2,500, per Epoch AI in August 2025 |
| Departmental server | Lenovo ThinkSystem SR650i V4 with two RTX PRO 6000 GPUs (96GB each) | One department's daily use, with Granite 4 H-Small, gpt-oss-120b or Jais-2-70B | $68,011 Lenovo price, June 2026 (Lenovo Press) |
| Enterprise server | Lenovo SR650a V4 with four L40S GPUs, or SR675 V3 with eight H200 GPUs | 70B-class and larger models for many users at once | $113,187 (4× L40S) or $397,802 (8× H200), Lenovo price, June 2026 (Lenovo Press) |
Lenovo prices ("usual sale price to the customer") come from a Lenovo cost paper, as of 15 June 2026. Street prices differ, and GPU prices rose in 2026, so we re-check before you buy.
Most pilots need one server-class GPU, such as one RTX PRO 6000 or one H100. IBM documents Granite 4 H-Small running on either (IBM). gpt-oss-120b fits on a single 80GB GPU (Hugging Face).
Document extraction can start smaller, on CPU plus one GPU. Arabic work with a 70B model needs two 80GB-class GPUs.
We do not resell hardware. You buy it in your own name, at cost, so ownership sits with you from day one. Software follows the same rule: an open-source stack by default, and vendor licences only if you choose them.
Not ready to buy? A pilot can run on a rented GPU in a cloud tenancy you control. Buy hardware after the pilot meets its success criteria.
How does a private AI pilot work?
A pilot covers one workflow on your own documents and takes two to four weeks after discovery. Success criteria are agreed in writing first. We install on your hardware or a rented GPU, test against those criteria and report the results. Then you decide whether to move to production.
- 1
Discovery, 3-5 days
We review one to three use cases, check your data and hardware, shortlist models and list each model's licence terms. You get a written recommendation, which may be to stay with a hosted service.
- 2
Written success criteria
Before any build, we agree in writing what the pilot must achieve. Examples are accuracy on a test set of your real questions, response time and who reviews outputs.
- 3
Install on your hardware or a rented GPU
We install on your hardware, or on a rented GPU in a cloud tenancy you control. Your documents go in only after you approve the setup.
- 4
Evaluation
We run the agreed test set and report the scores, including the failures. We also run the unplugged test described below with your team.
- 5
Decision
You choose: a production install, a change of scope, or a stop. The evaluation report is yours either way.
Written criteria matter. MIT research reported by Fortune found that about 95% of enterprise generative AI pilots produce little measurable profit impact (Fortune). A pilot without an agreed finish line is hard to judge.
Pick a back-office workflow with clear right and wrong answers for the first pilot. Policy questions, invoice fields and contract dates are good examples. Leave open-ended strategy work until the system has proven itself.
How do you prove nothing leaves the network?
You test it yourself. Our browser demo runs a small model on your own device. Switch off Wi-Fi after it loads, and it keeps working. On a pilot, unplug the network cable or block outbound traffic, use every feature and capture a traffic log. A private install should show no outside connections.
Try the offline demo in your browser. It downloads a small open model once, then runs on your graphics processor through WebGPU. A counter shows the network requests made while each answer is generated. You can check the same thing in your browser's developer tools.
The demo proves the method, not production quality. A server install runs a much larger model on your own hardware.
Buyers now arrive with a test. One published buyer's checklist asks vendors: "If I unplug the network cable, does every feature still work?" It also asks: "Can you provide a network traffic log from a running instance?" (Ertas).
Here is the test we suggest for any private AI system, including ours:
- Disconnect it. Unplug the network cable, or block all outbound traffic at your firewall. Then use every feature: questions, extraction, drafting, search and admin screens.
- Log the traffic. Record a full working session with your own firewall log or packet capture. Look for any connection that leaves your network.
- Check licensing. Nothing should need to call home to keep running.
- Check the settings. Model libraries should run in offline mode with telemetry off, for example with HF_HUB_OFFLINE=1 (Hugging Face docs).
- Check updates. New models and patches should arrive as verified bundles that your team imports.
- Check where logs live. Prompts, answers and audit logs should stay on your storage.
Run the test with your own tools, not ours. That way the evidence comes from your side.
What do the rules say in the GCC, UK, EU, Australia and US?
Few rules demand on-premise AI outright. Many residency rules can be met by in-country hosting, and the most sensitive data classes are where on-site or air-gapped systems fit best. Saudi Arabia, Qatar and Kuwait have the clearest rules. The UK, EU, Australia and US mostly regulate risk, access and dependence on providers.
| Region | What the source says | What it means for a deployment |
|---|---|---|
| Saudi Arabia | Current rules impose no general in-Kingdom storage requirement. A draft SDAIA amendment to the PDPL regulations would require controllers to store personal data inside the Kingdom. Consultation closes on 5 November 2026, and the draft is not yet in force (Clyde & Co). | In-Kingdom hosting, on premises or in a Saudi cloud region, would meet a storage rule of this kind. One compliance vendor reads the NCA's ECC as requiring remote monitoring centres for in-scope entities to sit in the Kingdom (Secure.com). We build and hand over, so operations stay with you. |
| Qatar | MCIT's Cloud First policy tells government agencies to move to endorsed cloud providers. Data classified C4, national security, is exempt from the policy (MCIT). | Most government work will run in an endorsed in-country cloud tenancy. Air-gapped installs suit C4 data and private firms that choose their own hardware. |
| Kuwait | Under CITRA's cloud rules, level 3 and level 4 government-sector data cannot leave Kuwait. This covers ministries, state-owned oil companies and the central bank (US trade.gov, 2022). | That data needs an in-country data centre or the organisation's own site. |
| UAE | The federal data-protection page describes no general localisation mandate (u.ae). OpenAI now offers in-country inference residency on dedicated UAE GPUs (Fast Company Middle East). | In-country cloud is a strong option. Choose on-premise for control or air-gapping, not for residency alone. |
| United Kingdom | The Government AI Playbook bars official information from public AI tools unless it is published or cleared for publication. It says own-infrastructure hosting means data "never leaves an environment that you own" (GOV.UK). HM Treasury designated four cloud and technology providers as critical third parties in July 2026 (Morrison Foerster). | Most UK public bodies use private hosted instances. Banks and insurers may want a self-hosted fallback model as part of exit planning. |
| European Union | Accenture found 62% of European organisations seeking sovereign solutions, rising to 76% in banking (Accenture). DORA has applied since 17 January 2025, with rules on ICT third-party risk (EIOPA). | Most EU sovereignty demand goes to EU cloud providers. We act as the implementation partner on open-weight models, on your site or in your EU tenancy. |
| Australia | The OAIC says "deploying a system locally or on premises is likely to be more privacy-preserving" (OAIC). APRA's April 2026 letter says "credible fallback processes are required" where AI supports critical operations (APRA). A privacy reform draft would clarify when storing or sending data overseas counts as a disclosure (Wotton Kearney). | Installs on your site, with no remote access from India, keep personal information onshore. A self-hosted model can also serve as a fallback for critical AI uses. |
| United States | CMMC Phase 2, with third-party certification at contract award, starts on 10 November 2026 (Schwabe). OMB memo M-25-22 bars vendors from training on government data without consent, and favours US-developed AI (Hunton). | Federal agencies can now buy FedRAMP-certified ChatGPT, Gemini and Perplexity (FedRAMP). Our fit is mid-sized healthcare, legal and finance firms. If your contract limits non-US staff, tell us on the first call; our engineers are in India. |
A summary of public sources as of October 2026, not legal advice. Check your obligations with your own counsel.
Sector rules add detail on top of these, especially for banks and hospitals. We list the rules your data falls under during discovery, and your counsel confirms them.
Is Webify.AI ISO 27001 or SOC 2 certified?
No. Webify.AI holds no ISO 27001 or SOC 2 certification today, and no other security certification. Our private AI work is designed so you do not need to trust us with data. We build on your hardware or tenancy, hand the system over, hold none of your data and keep no standing remote access.
- Your hardware or tenancy. The system runs on servers or a cloud account you own. Documents, prompts, answers and logs stay there.
- No copies with us. We hold none of your data. Test sets are built and kept in your environment.
- No standing access. Support runs through sessions you start, watch and end. Between sessions, we have no route in.
- Your controls apply. Access inside the system is role-based, and an audit log records who asked what.
- Updates you approve. New models and patches arrive as verified bundles that your team checks and imports.
- A clean handover. You receive the configuration, documentation and a model-licence register.
Certificates mainly shorten a security questionnaire. With this design, most of the questions are about your own environment, because the system runs inside it. Our security page covers contract terms and what we do not hold.
Model certifications are separate from ours. IBM says its Granite models are ISO 42001 certified. That covers IBM's model work, not Webify.AI.
What do you hand over after a production install?
Everything your team needs to run the system without us. That means the installed system and its configuration, an air-gap kit for updates, a model-licence register, the evaluation test set and a runbook. A retainer is optional. With one, we patch, refresh models and re-run evaluations through sessions you start.
- Air-gap kit: a mirrored container registry, verified model files, offline settings on by default and a written update routine.
- Model-licence register: each model's licence, attribution and naming rules, and use limits. Llama 4, for example, requires distributed derivatives to carry a name starting with "Llama" (Hugging Face).
- Evaluation set and report: your test questions, expected answers and scores, so your team can re-test after any change.
- Runbook: how to start, stop, back up, update and monitor the system.
Fine-tuning is a second step, not a default. Research finds retrieval beats unsupervised fine-tuning for adding knowledge (arXiv 2312.05934). We fine-tune only when evaluation shows a problem with format or behaviour.
Open models improve every few months. On a retainer, we test new models against your evaluation set every 6-12 months. We switch only when the scores justify it.
Frequently asked questions
Is private AI the same as an offline LLM?
Not quite. An offline LLM has no internet connection at all. Private AI also covers servers on your premises and dedicated in-country cloud tenancies. The engineering is the same; only the location and network rules change.
Do you need remote access to our systems?
No standing access. We install on site or through sessions you start, then hand over. Later support also uses sessions you open, watch and close. Air-gapped sites receive updates as bundles their own team imports.
Will Webify.AI hold any of our data?
No. Documents, prompts, answers, logs and test sets stay on your hardware or in your tenancy. We build and hand over; we do not host client data.
Is an offline model as good as ChatGPT?
Not on every task. Open models trail the best hosted models by a few months, as the research in the models section shows. On narrow tasks grounded in your documents, the gap is small. The pilot measures it on your own data.
Is private AI cheaper than a hosted API?
Rarely. Hosted open models are cheap, and self-hosting tends to pay only at high, steady volume. Choose private AI for control and compliance. The exception is a small model doing one high-volume task on one GPU.
Can we start without buying hardware?
Yes. A pilot can run on a rented GPU in a cloud tenancy you control, using documents your policy allows there. Buy hardware once the pilot meets its success criteria. When you do, it is bought in your name, at cost.
Which languages do you support?
English and Arabic lead, with Gulf-built models for Arabic work. Other languages depend on the model. We test each language on your own documents before committing to it.
How are models updated on an air-gapped network?
Through a written routine. New models and patches are downloaded on a connected machine, verified and moved in on approved media. We test each update against your evaluation set first, and your team imports it.
Do you work with IBM watsonx?
Yes. Webify.AI is an IBM Partner, and we can deploy on IBM watsonx where you want a vendor-supported stack. Otherwise we use an open-source stack built on vLLM.
Do you fine-tune models on our data?
Only when evaluation shows a need. Retrieval over your documents adds knowledge better and stays current. Fine-tuning helps with format, tone or a fixed extraction schema, and the tuned weights stay in your environment.
Can we see it working before we pay?
Yes. Try the browser demo with Wi-Fi switched off, then book the free 30-minute call. Paid work starts with discovery, after you have seen the method.
Where is Webify.AI based?
Webify.AI is an AI and software company headquartered in Ahmedabad, India. For private AI we build and hand over, so your data stays in your network or your country.
Book a free "Can this run offline?" call
Thirty minutes, a straight answer on whether your use case can run offline, and a rough sizing of the hardware it needs. No cost and no obligation.
- We sign your NDA before the call if you need one
- You leave with a written next step, even if the answer is "stay in the cloud"
- Pilots are fixed-price and run on your own documents