AI that ships inside the product, not bolted on beside it.
Practical model integration, retrieval pipelines, and workflow automation embedded directly into the systems your team already uses — evaluated against real tasks, not benchmark leaderboards. Built for startups and enterprise teams operating in Nepal.
Six capabilities, one accountable team
Model integration
LLM and vision APIs wired into existing products with fallbacks and cost controls.
Retrieval & RAG
Vector search over your own documents, grounded to reduce hallucination risk.
Workflow automation
Agentic pipelines that replace multi-step manual work, with a human checkpoint where it matters.
Evaluation harnesses
Task-specific eval suites so a model change is a measured decision, not a guess.
Fine-tuning & prompting
Prompt engineering first; fine-tuning only when the data and the economics justify it.
Guardrails & observability
Logging, cost dashboards, and content-safety checks on every model call.
Four phases, each with a named deliverable
Scope
The highest-leverage use case identified and an evaluation plan agreed upfront.
Prototype
A working integration against real data, scored against the eval harness.
Harden
Guardrails, cost controls, and fallback paths built before general release.
Operate
Model performance and cost monitored, with a review cadence for drift.
// eval suite · support-triage-v3model = "gpt-5.1-mini"cases_run = 312accuracy = 0.94p95_latency = 820verdict = "promote"
Every model change measured before it ships
- Grounded in your data — retrieval, not memorized guesses, for anything factual.
- Evaluated before shipping against a task-specific harness, not a generic benchmark.
- Cost and latency budgeted up front, monitored continuously after launch.
Built into every engagement
AI engagements on the record
SaaS platform — support-ticket triage
LLM classifier cut first-response time 62% across an 8-agent support desk.
Questions we get about AI integration
How do you decide which AI feature to build first?
A one-week scoping phase identifies the highest-leverage use case in your product and agrees an evaluation plan before any integration work starts — so effort goes toward the feature with real impact, not the flashiest demo.
How do you prevent hallucinated or wrong answers?
Retrieval is grounded against your own documents rather than relying on a model's memorized knowledge for anything factual. Every model change is scored against a task-specific evaluation harness before it reaches production.
Do you build with a specific model provider?
No — integrations are typically wired with fallback paths across multiple providers, so a single vendor's pricing change or outage doesn't take the feature down with it.
How is cost controlled?
Cost and latency budgets are set during the harden phase, with logging and cost dashboards on every model call so spend is monitored continuously after launch, not discovered at the end of the month.
What does the evaluation process look like?
A task-specific eval suite runs real cases against the model before promotion — accuracy, latency, and failure modes are measured, so a model change is a documented decision rather than a guess.