Technology and AI Lawyers in Georgia
Technology contracts should allocate control over data, intellectual property, performance, security and exit. AI adds questions about training inputs, outputs, human oversight, transparency and vendor dependency.
What our technology & ai work covers
We advise Georgian technology companies and customers on software development, SaaS, cloud, licensing, outsourcing, platform terms, data use, cybersecurity allocation, AI procurement and commercialisation. The legal framework is assembled from contract, data, IP, employment, consumer and sector rules relevant to the product.
Legal and commercial context
Georgia does not need a single AI statute for AI projects to create legal obligations. Existing contract, personal-data, intellectual-property, employment and sector rules still govern inputs, outputs, representations, confidentiality, security and responsibility. EU-facing products may also require separate EU-law analysis.
Vendor descriptions should be tested against the technical deployment. Counsel needs to know whether customer data is retained or used for model improvement, where processing occurs, which subprocessors are involved, whether outputs are reproducible and what happens when the service or model changes.
Scoping the decision, evidence and completion record
At the start of this instruction, counsel separates the immediate commercial decision from longer-term remediation. For technology & ai, the initial workstreams usually connect software transactions, aI procurement and data and security. They are sequenced around the first agreed step—map the product, users, data, vendors and target jurisdictions.—so management knows which conclusion is needed now, which issue is a dependency and which improvement can follow after the transaction or operating decision.
The evidence file should remain intelligible to a director, investor, bank, auditor or regulator who was not present during the original discussions. It therefore links product and architecture description, source-code and contributor records, dataset and content provenance and vendor and subprocessor list to the factual assumptions and applicable public sources. Counsel tests that record for risks such as IP ownership is assumed rather than assigned, vendor may reuse confidential inputs and AI output warranties exceed technical evidence and records unresolved points rather than silently treating them as confirmed facts.
Completion is defined by usable output, not the delivery of a generic memorandum. Depending on scope, the closing record will include technology contract suite, AI contract schedule and IP chain-of-title report and an implementation list showing approvals, signatories, filings, notices, owners and dates. Any conclusion that depends on tax, accounting, technical evidence or foreign law is identified with the responsible specialist and the date on which that dependency must be resolved.
Workstreams designed around the business decision
Software transactions
Draft development, SaaS, cloud, support, implementation, escrow and licensing agreements.
AI procurement
Allocate permitted use, input rights, output treatment, evaluation, oversight, change and incident responsibilities.
Data and security
Coordinate data roles, locations, subprocessors, security commitments, breach support and deletion.
IP chain
Confirm employee, contractor and third-party rights in code, content, datasets, brands and documentation.
Platform terms
Prepare business terms, acceptable use, complaints, suspension, payments and liability for digital services.
Exit resilience
Address portability, export formats, transition assistance, continuity and deletion when the relationship ends.
How the legal work is organised
- 1
Map the product, users, data, vendors and target jurisdictions.
- 2
Identify legal roles, IP chain, regulated functions and material technical assumptions.
- 3
Prepare or review the contract and a concise risk/decision schedule.
- 4
Coordinate technical, security, tax and foreign-law confirmations with their owners.
- 5
Complete signing, implementation controls and a review trigger for model or product changes.
Documents and evidence to prepare
The exact request is tailored to the matter. A first review commonly starts with:
- product and architecture description
- source-code and contributor records
- dataset and content provenance
- vendor and subprocessor list
- security and incident materials
- service levels and acceptance criteria
- customer terms and privacy notices
- AI evaluation, oversight and change records
Risks we test
Legal review focuses on consequences that can affect authority, value, timing, compliance or enforceability:
- IP ownership is assumed rather than assigned
- vendor may reuse confidential inputs
- AI output warranties exceed technical evidence
- service changes without customer control
- data roles or locations are unclear
- termination provides no usable export or transition
Typical deliverables
The agreed deliverable should help the company act, obtain approval and retain a reliable record of the decision.
Official public sources
These links are starting points for the current public legal framework. The operative consolidated text, amendments and facts should be checked when advice is given.