46%Support deflection
+18%CSAT
Emerging Technology
AI Development Services
RAG assistants, copilots and agents grounded in your data - measured, guarded, shipped.
evals before demos
Charcoal particle field with sparse orange sparks forming a loose sphereProblems solved
What this removes.
manual repetitive work
ops hours burn on copy-paste
AI pilots that never ship
budget burned on demos
unguarded model risk
hallucination + compliance liability
data not AI-ready
retrieval returns garbage
no evals
regressions discovered by customers
What's included
What you get.
- AI readiness audit
- data pipeline prep (chunking + embeddings)
- RAG pipeline with citations
- LLM copilot / agent builds
- eval suite + guardrails
- MLOps (versioning + monitoring)
- cost / latency bench report
- fallback + human-in-the-loop flows
- team enablement workshops
- 90-day improvement SLA
Scope
Scope, clearly drawn.
Core scope is fixed. Add-on prices are indicative — not quotes.
Included in every engagement
- readiness audit + success metrics
- data prep + retrieval build
- prototype with guardrails
- eval suite wired into CI
- productionize (MLOps + monitoring)
- enablement + handover
Optional add-ons
- fine-tuning sprint (only if RAG insufficient)+$8k
bench-gated decision
- on-prem open-weight deployment (vLLM/Ollama)+$6k
- red-team + guardrail hardening+$5k
- AI usage policy + compliance pack+$4k
Our approach
How we run it.
Audit
data + risk map, success metric picked
Prototype
RAG first, citations always
Evals + guardrails
gates before any demo
Productionize
MLOps + fallbacks + HITL
Monitor
drift + cost from day one
Pricing signal
What it typically costs.
Typical range
$30k-$200k
Midpoint $115k · final quote after a fixed-scope discovery.
evals before demos, always
Technologies
Tools we reach for.
Case evidence
Proof.
Our team's work, before DosVectis.
FAQ
Questions, answered.
Which LLMs do you use?
Task-based bench - open-weight and frontier; the scoreboard decides.
Do you train models?
Fine-tune rarely; RAG first, tuning only when bench proves it.
Is our data safe?
Private infra by default; no public API calls without DPA + masking.
How do you measure success?
Eval suites in CI plus business KPIs (deflection, CSAT, time-saved).
Open or frontier models?
Both benched; open-weight wins on TCO when quality holds.
What if retrieval fails?
Confidence thresholds, citations, fallbacks and human-in-the-loop.
Related services
Pairs well with.
AI Development Services without the guesswork.
RAG assistants, copilots and agents grounded in your data - measured, guarded, shipped.