Amber: permissioned project knowledge before autonomous quoting

External-source research synthesis, not an OfferSignal client result. Based on the retained October 2026 research and source-specific dates.

An industrial business may have the evidence for a new enquiry somewhere in SharePoint, email, network drives, project folders or a business system, but still lose time finding the approved drawing, revision, material note or service history. Amber is aimed at that knowledge problem rather than at being a generic public chatbot. Its practical promise is to make fragmented internal evidence searchable while preserving the permissions attached to the source.

What Amber connects and returns

Amber describes a proprietary AI Data Layer that synchronises and indexes text and vectors from sources including SharePoint, Teams, Confluence, Jira, network drives, email and websites. A query is answered with retrieved context and source references, subject to the querying user’s existing permissions. The product pages also describe an AI search, assistant, specialised agents, multi-model access, analytics, REST API, WebDAV, IMAP and Model Context Protocol interfaces, alongside Teams, desktop and mobile access, a Chrome extension, an Outlook add-in, widgets and workflow-tool actions. 1

This is a useful fit for non-SKU project knowledge: drawings, specifications, revisions, engineering correspondence, equipment records, prior work and undocumented procedures. Amber reports customers or implementations in manufacturing, engineering, information-technology consulting and consumer goods, including Scheidt & Bachmann, Ritter Sport, Zentis, Schüßler-Plan, Dalli and Hailo. That does not establish tolerance-aware quoting, computer-aided design or bill-of-material interpretation, live capacity checks, material compatibility or binding commercial offers. Those are separate tests, not consequences of the word “agent”. 2

The named DB Regio test

The clearest public use case is a Deutsche Bahn DB mindbox challenge. In 2023, amberSearch won a pitch for a 100-day proof of concept intended to help DB Regio service staff answer repetitive customer enquiries by finding previously resolved information and drafting responses. Amber says the cooperation continued after the programme. The public case does not disclose before-and-after response time, accuracy, deflection, production volume or a causal evaluation. It is therefore evidence of a named real use case and a continuing relationship, not a quantified production result. 3

Other figures are provider-reported. A 2025 report repeats claims of integration at more than 200 small and medium-sized enterprises and search-time reduction of 40% or more, without a denominator, measurement period, control group or customer-authored validation. In 2026, Amber reported more than 400 implementations or customers; that count has a different denominator and date and is not independently audited. Coverage also reports Amber’s claim of up to 60% token-cost reduction depending on the use case, explicitly without a disclosed benchmark, model mix, workload or control. 4 5 6

From search to agents, carefully

Amber began as an AI enterprise-search product around 2020, launched as a business-to-business startup in 2021, and expanded into amberSearch, an assistant or company-GPT layer, agents and the Data Layer. It announced a €2.1 million seed round in March 2025 and a €7 million Series A in August 2026, co-led by Ventech and NRW.Venture, with European expansion and greater autonomy described as next steps. 2 4 6 7

That evolution matters commercially. The current product can be assessed as configured, permission-aware retrieval and controlled workflow action. The Series A material describes movement from user-driven work toward proactively recognising and executing work; that is a roadmap direction, not proof that unattended execution is generally available. Public starter pricing is €12.99 per user per month, with chat, agents, API access, European hosting and stated action allowances. A 30-day no-card trial and credit plans are advertised, while implementation, connector, private-instance, migration and overage costs remain unclear. 8 9

An illustrative industrial pilot

For an illustrative electrical, modular, food, metals or machinery workshop, start with read-only retrieval over a bounded corpus: approved drawings and revisions, project evidence, materials, equipment and capacity notes. Require citations, document dates, units and an explicit “indicative estimate” label. Engineering remains authoritative for feasibility, commercial owners for price and terms, and named approvers for supplier commitments, production release and customer communication.

Replay representative enquiries containing stale revisions, near-duplicates, missing units, incompatible materials, prompt-injection text and duplicate workflow requests. Measure authoritative-record retrieval, wrong-revision rate, unanswered-question rate, review time and exception rate, recording the task set, period and pilot status. Compare Amber with an existing Microsoft or enterprise-resource-planning search route on the same corpus. Only after a read-only result is understood should narrowly allow-listed actions be enabled, with audit logs, idempotency checks, rollback and human approval.

References

Sources and next reading

• Amber product overview — public product information; customer data requires an authenticated tenant and configured connectors.
• Amber free trial / platform — public sign-up entry; company email and account required; treat as a controlled trial, not open data access.
• Amber personalized demo — public demo request; form or contact interaction required.
• Amber pricing — public pricing page; full enterprise implementation and connector terms may require sales engagement.
• DB mindbox case — public case description; no customer tenant or production-data access.

Explore OfferSignal · Focused Business & AI Workshop · All research