Build a business-specific AI system your team can evaluate, deploy, and extend.
Build a business-specific AI system your team can evaluate, deploy, and extend.
Turn approved knowledge, business rules, examples, controlled memory, and tools into a versioned harness. When evidence supports it, Ahzi separately evaluates fine-tuning or model-weight adaptation and exports eligible artifacts under the selected license and runtime.
The retrieval, controlled memory, tool, and trace design
A base-model, adaptation, and artifact-ownership decision
Buyer brief
Turn scattered operating context into an owned AI system.
Start with the business context that isolated prompts fail to reproduce, then define the smallest owned system that can be tested and extended.
What the review covers
Structures approved rules, examples, and operating context as an explicit business ontology
Versions retrieval, controlled memory, tools, and trace capture inside one harness
Evaluates a base-model, fine-tuning, or model-weight adaptation path before release
Defines exportable checkpoints, adapters, or model weights only where the license and runtime permit ownership
Questions for the workflow owner
01Which business decisions, terms, and examples must the system represent?
02Which approved sources and controlled memory should shape its responses and actions?
03Which model artifacts must remain portable, and what evidence should govern release?
Target outcome
A scoped harness with explicit business structure, controlled memory, representative evaluations, and portable artifacts where ownership is permitted.
Core offerings
AI transformation from system design through team ownership.
Senior AI engineering for production agents and custom development, grounded in two decades of Salesforce and cross-stack delivery. Ahzi builds custom harnesses and business-specific model systems, then trains your team to evaluate, release, and extend them.
04 / 05
01SelectWorkflow Selection ReviewChoose a workflow with a clear owner, boundary, and release decision.
Best fit
Operations, product, or CRM owners choosing where an agent should enter a real process.
What Ahzi builds
Ahzi traces one process from trigger to decision, tests the riskiest assumption, and defines the smallest useful agent boundary.
Systems and data
The review stays read-only across representative records and process artifacts.
Ownership and controls
The workflow owner approves the boundary, names forbidden actions, and signs the build or no-build decision.
Deliverables
Candidate workflow map with a named trigger and owner
System, data, permission, and exception boundary
Representative evaluation plan and first release decision
04OwnBusiness-Specific AI HarnessTurn business knowledge and operating rules into a versioned AI system with controlled memory that your team can evaluate, deploy, and extend.
Best fit
Teams that need business-specific behavior, controlled memory, and portable AI assets instead of another isolated prompt.
What Ahzi builds
Ahzi structures the business ontology, retrieval and memory layers, and, where justified, a separately evaluated fine-tuning or model-weight adaptation path, tool harness, evaluation suite, and deployment interface.
Systems and data
Approved domain corpus, business ontology, retrieval and memory stores, base model or owned checkpoint runtime, tools, evaluation data, and deployment controls.
Ownership and controls
The customer owns the approved corpus, business structure, evaluation set, release thresholds, and every exportable artifact defined in scope.
Deliverables
Versioned agent harness with retrieval, memory, tools, and trace capture
Business ontology, memory schema, and model adaptation recipe
Exportable checkpoints, adapters, or model weights when the selected license and runtime permit ownership
05OperateAgent Operations and Engineering TrainingTurn traces and failures into a release system the engineering team can operate and extend.
Best fit
Teams with an existing agent or AI feature that need a dependable quality loop and engineers who can operate and extend it.
What Ahzi builds
Ahzi instruments the run, builds the case set and rubric, connects failures to regression tests, and trains the team on the harness, release gate, incident path, and next build cycle.
Systems and data
Existing agent runtime, traces, evaluation data, CI or release workflow, human review queue, and production telemetry.
Ownership and controls
Domain reviewers calibrate scoring, own release thresholds, inspect flagged traces, and authorize rollback or expansion.
Deliverables
Trace schema and failure taxonomy
Versioned evaluation dataset with deterministic and judged checks
Release gate, operating runbook, and hands-on agentic engineering workshop using the team’s system
Proof from shipped work. Controls for the next build.
Delivery evidence sits beside the permissions, approvals, evaluation, and rollback path.
Delivery proof
Completed contract intelligence delivery
Contract data at population scale
Ahzi has run contract intelligence across more than 5,000 agreements with population-level verification.
Salesforce and cross-stack delivery
AI connected to real enterprise systems
Two decades of custom software work, including Salesforce delivery across CRM, applications, APIs, data platforms, and cloud infrastructure, shape how Ahzi connects AI to existing systems.
Live Ahzi toolchain
Operator-owned AI tooling
Internal MCP tools, review queues, benchmarks, evaluation runners, and custom agent harnesses run Ahzi operating workflows.
Enterprise operating experience
Enterprise delivery context
AI delivery inside a utility-scale enterprise informs the permission, ownership, exception, and release controls used in the build process.
Build controls
Context boundary: approved sources and purpose-specific access
Action boundary: reversible tools and approval for consequential writes
Quality boundary: representative cases, deterministic checks, and domain review
