Help your team find the right information, prepare useful drafts and move everyday work forward. We design and implement AI around your business: the people, documents and systems you already rely on.
A useful implementation starts with a specific job. We work with you to choose a workflow, connect the right information and define what a good result looks like.
Give staff a simpler way to consult approved procedures, product information and internal guides. A document-connected implementation can show the source and flag when it cannot find enough information.
Pilot idea: answer common new-hire questions from one approved handbook.
Prepare customer updates, meeting follow-ups and internal summaries from information your team supplies. Keep review with the person responsible for the final message.
Pilot idea: turn project notes into a consistent daily update.
Connect selected tools so an assistant can inspect information, prepare files or help with an agreed sequence of actions. We define access and review requirements before connecting business systems.
Pilot idea: assemble a draft handoff from an approved project folder.
Structure supplied notes into decisions, tasks and items that still need an owner. Evaluate the result against real work before expanding the rollout.
Pilot idea: create a meeting action list without inventing dates or owners.
Our ambition is to connect useful intelligence to the way a business actually operates. Halo explores action across tools and devices. Nova provides a workspace for conversation and model-assisted work. Alongside those products, we want to build customer systems that understand approved company information, fit existing workflows and improve through measured results.
Simpler onboarding, clear model availability and understandable errors are part of the product. We want people to know what their assistant can do, what it needs from them and who to contact when something fails.
Explore assistants connected to approved procedures, project records and internal knowledge, with source references and permissions that follow the person using them. Give teams a way to keep that information current and correct mistakes.
Improve continuity between conversations, devices and supported business tools. A future connection between products should preserve task context, show what has completed and make human review clear before consequential changes.
Continue investigating model customization, specialized workflows and self-hosted options. Use approved examples and separate evaluation tasks to compare usefulness, reliability, response time and cost before expanding a deployment.
These are goals and areas of exploration. They are not guaranteed features, delivery dates or claims that Halo and Nova already share one integrated workflow. The recordings above show the capabilities demonstrated today.
Halo and Nova are product options. A customer deployment also needs the right data, access, integrations, training and ongoing care. Your written scope defines which pieces we deliver.
One workflow, a named business owner, approved sample information and a clear success checklist. Measure usefulness, accuracy and review time against the way your team works today.
Agreed document sources, user roles and connected systems. We document where information is processed, how sources stay current and who can access the resulting system.
An agreed interface, representative workflow testing, staff onboarding and a handover guide. The pilot includes examples of good answers, missing information and requests the system should decline.
Named ownership for updates, source refresh, backups and issue handling. Recurring hosting, model usage and support are identified separately so you understand what happens after launch.
Tell us who does it, what information they use and what a finished result should look like. We will help identify a practical first scope.
Discuss your workflow ↗Retrieval-augmented generation, or RAG, finds approved company information and supplies it to a model when answering. It supports source citations and refreshed documents without changing model weights. We usually evaluate this first for internal knowledge.
Train an existing model further on approved examples when the evaluated task needs more consistent behavior, formatting or classification. This requires suitable data, a compatible model license and a separate evaluation. It does not replace access controls or current source information.
Build the screens, identity integration, document connectors, review queues and reporting your team needs. Start with read access; changes to business records follow an agreed permission and human approval process.
Training a foundation model is a separate research and infrastructure engagement. It requires its own feasibility study, data strategy, compute budget and delivery plan; it is outside the implementation ranges below.
These preliminary Glass Tech planning ranges describe a purchase-based deployment. They are estimates for the stated scope, not manufacturer quotes, fixed offers or guaranteed delivery dates. A workload review and written proposal establish the actual price.
| Starting scope | Hardware | Implementation | Combined initial | Deployment |
|---|---|---|---|---|
| Private AI pilot ↗ | $5,000–$10,000 | $8,000–$20,000 | $13,000–$30,000 | 2–4 weeks |
| Department AI deployment ↗ | $15,000–$35,000 | $20,000–$60,000 | $35,000–$95,000 | 4–8 weeks |
| Integrated company AI ↗ | $50,000–$150,000+ | $60,000–$150,000+ | $110,000–$300,000+ | 8–16+ weeks |
Timeframes start when the scope is approved, hardware is available, data access is authorized and usable samples are supplied. Procurement lead times and delayed client review extend the calendar. Allowances exclude tax, travel, recurring support/software/cloud costs, major data cleanup, electrical/cooling changes and separately designed high availability. Hosted options replace some hardware purchases with recurring infrastructure charges and require their own quote.
One approved document source, one defined workflow and a small evaluation group.
Hardware approachCompact local AI system, such as an NVIDIA DGX Spark, or a qualified GPU workstation. Selection follows a model and workload test.
Acceptance checkAnswer representative questions with sources, reject restricted documents, measure response time and record the pilot decision.
Explore package details ↗Several agreed sources, identity integration, permission-aware retrieval and one department workflow.
Hardware approachConfigured professional GPU workstation, including Lenovo ThinkStation options, plus backup and power protection as scoped.
Acceptance checkVerify user permissions, citation quality, simultaneous requests, content refresh, recovery and administrator handover.
Explore package details ↗Multiple departments or business-system integrations with stronger operating and recovery requirements.
Hardware approachValidated multi-GPU server architecture, including Lenovo ThinkSystem candidates. Power, cooling, storage and networking are designed together.
Acceptance checkTest each integration, approval boundaries, load, recovery and the agreed operational monitoring. High availability is a separately scoped design.
Explore package details ↗Allow an additional $10,000–$35,000 in implementation and 3–6 weeks for a bounded task with usable approved examples and held-out evaluation. Suitable existing compute may be reused; additional training compute is quoted separately.
We recommend this phase only after a baseline test demonstrates a problem that training can reasonably address.
Local hosting alone does not establish privacy or compliance. Each design records where prompts, documents, logs, backups and support access reside. Model answers can be wrong; the workflow includes review and escalation appropriate to the task.
