Fine-tuning / Model Derivation
We derive and adapt base models to your domain so they respond with precision, consistency, and control, ready for production with metrics and governance.
- •End-to-end process: dataset design → curation → training → evaluation → packaging → deployment.
- •Domain adaptation: terminology, style, internal policies, processes, and real business cases.
- •Quality and evaluation: internal benchmarks, regression tests, behavior validation, and acceptance criteria.
- •Guardrails: output policies, hallucination mitigation, protection against malicious prompts, and security control.
- •Versioning and traceability: which data/criteria produced each model version and how it evolves over time.
- •Cloud or local: we also build platforms optimized to run models locally (on-prem/edge/air-gapped), prioritizing efficiency (quantization, caching, batching) and operational stability.
ResultModels aligned to your business and operable in production: more reliable responses, less risk, measurable improvements per version, and controlled deployments in both cloud and local environments.
Agent Network
We design an agent network that executes real business tasks: orchestrates tools and data, and takes action with permissions, auditing, and observability from day one.
- •Multi-agent orchestration: planning, role-based execution, multi-step flows, queues, and events.
- •RAG & knowledge: retrieval, chunking, indexes, freshness, response evaluation, and source control.
- •Permissions and security: scopes per role, least privilege, human approvals when appropriate.
- •Audit and traceability: logging of actions, inputs/outputs, and agent decisions.
- •Full observability: latency per stage, cost per interaction/task, success rate, and friction points.
- •Flexible deployment: cloud/hybrid or local, bringing compute and data closer when privacy or cost requires it.
ResultAgents that actually execute work and generate value: real automation, traceability of every action, cost control, and measurable operation (latency, success, failures) to scale without losing governance.
Voice AI
We build production-ready voice assistants integrable with telephony and contact-center infrastructure, optimized for low latency, concurrency, and monetization.
- •Conceptual architecture: audio/call input → gateway/streaming → agent → tools → monitoring.
- •Low-latency design: bidirectional streaming, jitter control, timeouts, and audio quality.
- •Concurrency and scaling: sizing by concurrent calls, per-client limits, and capacity control.
- •Multi-tenant: each client with its own agent, policies, knowledge, and independent routing.
- •Monetization: per-minute measurement, usage limits, prepay/postpay, and per-client reports.
- •Optional local execution: on-prem platforms for sensitive environments or to reduce cost per interaction.
ResultSmooth, scalable voice experiences: controlled concurrent calls, stable latency, measurable costs per client/minute, and a base ready to grow to multiple clients without reinventing the architecture.
MCP Servers
We build secure enterprise integrations so your agents connect to your ecosystem (apps, databases, ITSM, repos, calendars, CRMs) with permissions, auditing, and control.
- •Connectors and integrations: internal systems, APIs, SaaS, engineering and operations tools.
- •Permission model: control by action and by resource, with separation by roles/teams/clients.
- •Audit and compliance: traceability of actions and evidence for regulated environments.
- •Usage and cost observability: consumption per tool, per agent, and per client/tenant.
- •Productive operation: HA, rate limiting, isolation, and hardening.
- •Cloud or local: integrations executable inside private networks or restricted environments.
ResultProduction-ready integrations: agents operating with correct permissions, auditable actions, lower operational risk, and consumption visibility to scale connectors without losing control.
Key principles
- •Security and privacy by default: encryption, per-client isolation, secrets control, and data minimization.
- •Traceability and governance: model/prompt/dataset versioning, action auditing, and approvable flows.
- •Cost control: budgets, per-client limits, per-use-case measurement, and inference optimization.
- •Operability in production (Day 2): observability, runbooks, incident response, and continuous improvement.
- •Cloud/local portability: we design to run in cloud, hybrid, or 100% local, with platforms optimized for on-prem/edge inference.
ResultOperable and governable AI: real security, full auditing, costs under control, and consistent deployments (cloud or local) that hold up over time.
