Seven capability lanes — from data pipelines and ML models to RAG systems, AI agents, and full-stack applications. Fixed scope. Fast delivery. A handoff your team can own and extend.
Practice areas
Each practice area has its own page — scope notes, reference architecture, delivery phases, and governance expectations. Pick a lane or request a cross-capability briefing.
Clean data your models can trust.
Models are only as reliable as the data they train and serve on. We build ingestion pipelines, transformation layers, and feature stores with documented contracts, quality gates, and lineage — so your ML team works with data they can actually trust, not firefight.
Insights that turn into product decisions.
We run structured experimentation programs — forecasting, classification, segmentation — with reproducible baselines and honest assessments of what should graduate to production. No vanity metrics. No endless exploration without a ship date.
Models that survive outside the notebook.
Getting a model into production is an engineering challenge, not a research one. We build the training pipelines, serving infrastructure, drift monitors, and MLOps loops that keep your models accurate and auditable as real-world data shifts.
Retrieval systems your users can trust.
Naive vector search gives confident wrong answers. We build citation-backed RAG with hybrid retrieval, cross-encoder reranking, and confidence scoring — so every answer traces to a verifiable source and operators can tune relevance without touching model weights.
Agents that hold up under production load.
Production agents fail on tool reliability, not model quality. We design multi-step agents with explicit policies, idempotent tools, evaluation harnesses, and human escalation paths — so you can ship, measure, and iterate without surprise regressions.
LLM features engineered for growth.
Beyond an API call. We wire LLMs into your product with caching, intelligent routing, streaming UX, eval gates, and cost controls — so you scale traffic and swap providers without rewriting core logic or compromising on quality.
End-to-end AI products your users rely on.
AI demos bolted to brittle backends don't survive real users. We design and build the APIs, web surfaces, and AI features that tie your model layer to a working product — clear ownership boundaries, CI/CD-ready, and a handoff your engineers can run.
Engagement models
Different problems call for different commitments. Each model has a written scope, transparent pricing, and the same delivery standards.
01
A focused discovery and prototype to de-risk one lane. You get architecture decisions in writing and a working slice on staging — before any major build commitment.
Timeline
2–6 weeks
Investment
Comparable to one month of a senior staff engineer, fully loaded.
02
End-to-end delivery of a single capability hardened for real users. Includes evals tied to your KPIs, observability, runbooks, and a clean handoff your team can operate without us.
Timeline
6–12 weeks
Investment
Comparable to a senior hire's first quarter — without recruiting lead time or ramp cost.
03
A blended pod working alongside your engineers across multiple capability lanes. Shared backlog, weekly demos, continuous outcomes — not a set-and-forget retainer.
Timeline
Quarterly retainers
Investment
Sized to a small dedicated AI team, billed quarterly — no hiring drag, no severance exposure.
Not sure which model fits? A 30-minute scoping call gives you a written recommendation — including a budget shape sized to the outcome, not to our capacity.
Included in every engagement
Capability lanes deliver the headline outcome. These cross-cutting practices are what make the outcome durable once we hand it off.
Trade-off memos, sequence diagrams, and an ADR log — so engineering, security, and finance can review the path before any code is committed.
Offline suites seeded from real failure modes, online sampling, and dashboards that reflect the metrics you already report on — not demo accuracy.
Tracing, structured logs, cost and latency budgets, and alerting wired to your incident tooling — shipped alongside the feature, not retrofitted six months later.
Data handling, retention, redaction, and role-based access modeled with your security team during architecture — not bolted on at launch when it's expensive to change.
Runbooks, environment guides, and architectural notes written for the engineers who will own this system after we hand it off. Readable by people, not just AI.
Pair programming sessions, recorded walkthroughs, and readiness checklists. Your team should operate the system independently on the first day — that's the bar.
Delivery process
The same phased model applies across all capability lanes. Milestones map to artifacts your engineering, security, and finance stakeholders can review and approve.
Week 1
Outcomes, constraints, data realities, and success metrics agreed in writing before scoping begins. No assumptions go undocumented.
Weeks 1–2
Architecture options with explicit trade-offs, a fixed proposal, and a risk register your stakeholders sign off before any code is committed.
Weeks 3–8
Incremental releases behind feature flags, weekly demos, and offline evals run against your acceptance criteria — not internal vanity metrics.
Weeks 8–12
Load testing, observability, security review pack, and runbooks for the incidents you can predict — and a plan for the ones you can't.
Post-launch
Documentation, KT sessions, and a written hypercare window. Clean exits or long-term retainers — your choice after you've seen the work.
Outcomes
These ranges are benchmarked against published research and comparable deployments — not specific Vegrade client results. Final targets are agreed and written into the SOW before build begins.
60–80%
Faster document first-pass review
Target · Citation-backed M&A diligence RAG
1.5–2.5×
Qualified conversions from outbound
Target · Policy-bound AI lead agent
50–70%
Tier-1 ticket deflection
Target · Omnichannel support agent
6–12
Weeks to production
Typical across all capability lanes
Technology platform
We meet teams where they are and extend what you already operate. Stack choices are scoped per engagement against your security, latency, and cost constraints — we don't lock you in.
Data & infrastructure
Machine learning & MLOps
LLMs, RAG & agents
Product, ops & observability
FAQ
Ready to build?
We'll map the right capability lane, identify dependencies, and share a written scope with budget shape — before you commit to anything.
