Varanasi, UP- IN
Model Tuning & PromptOps
Higher accuracy, lower cost, and safer outputs—backed by versioning, evaluation, and observability.
Model Tuning & PromptOps
We make AI features reliable in production. That means choosing the right foundation model, shaping high-quality datasets, and using parameter-efficient tuning (LoRA/PEFT) where it actually moves the needle. On top, we implement PromptOps: reusable prompt templates, strict versioning, automated tests, and real-time monitoring so changes improve outcomes instead of breaking them.
Our approach reduces hallucinations, latency, and token spend while improving task accuracy and policy compliance. Pipelines include data governance (PII handling, consent, retention), offline/online evaluation, safety guardrails, and canary releases with instant rollback. We integrate seamlessly with your stack—APIs, RAG/search, analytics—and keep ownership of your data clear and compliant (DPDP/GDPR).
Tuning That Targets Business KPIs
We start by defining success metrics (exact-match, BLEURT/semantic similarity, grounded-answer rate, escalation rate, cost per task, p95 latency). Then we design evaluation sets from real use cases—customer emails, support flows, domain documents—de-duplicated and labeled. Where prompts can’t reach target quality, we apply instruction fine-tuning or adapters (LoRA/PEFT) to open-source or private models. Guardrails (content/PII filters, policy rules, tool-use constraints) run before and after generation. The result is measurable uplift on the tasks that matter—fewer escalations, faster resolution, and lower unit cost.
PromptOps: Versioned, Testable, Observable
Prompts are code. We treat them that way: parameterized templates, Git-backed versions, feature flags, and release channels (dev/stage/prod). Every change runs through an eval harness (offline tests + shadow traffic) and ships via canary with automatic rollback on regressions. We log prompts, model configs, tool calls, and outputs with trace IDs for full observability and debugging. Cost controls include token budgets, response truncation rules, caching, and multi-model routing (fast/cheap for simple requests; accurate/grounded for complex ones).
Frequently Asked Questions
We fine-tune when prompts and retrieval can’t reach KPI targets. If accuracy gaps are narrow or data is scarce, we prioritize prompt/routing improvements first.
Hundreds to thousands of high-quality examples per task. We help curate, anonymize, and balance datasets; PII handling follows DPDP/GDPR principles.
Both hosted and open-source models. We select based on quality, latency, cost, compliance, and deployment constraints; adapters enable quick iterations.
Task-specific offline evals, red-team prompts, hallucination/grounding checks, and policy tests. In production we track accuracy, latency, cost, and incident rates.
Token budgets, short/structured prompts, caching, selective tool use, and model routing. Where feasible, we distill to smaller models without losing accuracy.
Yes. We connect to your search/vector stores (for RAG), CRMs/ERPs, data lakes, and observability stack. Everything is instrumented and auditable.
Data stays in agreed regions; we minimize and mask PII, encrypt at rest/in transit, and document controls for audits. Access is least-privilege and time-boxed.
After discovery and eval-set prep, prompt-only iterations deploy continuously. For tuning, expect a short loop: data prep → train → eval → canary → roll out.
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