01 · Northwind / Services
Different workflow.
Same underlying stack.
Agents, retrieval, document AI and forecasting all draw on the same eight capabilities underneath. Here is how they combine into each service, and what each one leaves running in production.
8
Capabilities
6
Services
1
Evaluation bar
02 · What’s underneath
Eight capabilities.
Six ways we combine them.
01
86%
LLM orchestration
Routing, retries and fallbacks across models
Agents
Knowledge
Enablement
02
58%
Retrieval & vector search
Hybrid search with evaluated top-k
Knowledge
Agents
03
41%
Agent frameworks & tool use
Typed tools, scoped permissions, audit logs
Agents
04
47%
Document extraction & OCR
Field-level confidence on every value
Documents
Knowledge
05
19%
Forecasting & time series
Demand and capacity models with drift checks
Data & MLOps
06
72%
MLOps
Versioned releases, rollbacks in minutes
Data & MLOps
Agents
Documents
07
78%
Data pipelines & warehousing
Lineage from source to every answer
Data & MLOps
Strategy
Documents
08
100%
Evaluation & guardrails
A gold set and a bar before anything ships
All six
Bar = share of production systems using the capability
64 systems · Sept 2026
03 · The six services
Each one draws on
a different slice of the stack.
Strategy
01
Strategy
2–5 weeks
AI Strategy & Readiness
A structured assessment of your data, infrastructure and use cases, ending in a prioritized 12-month AI roadmap.
Data & infrastructure audit
Use-case prioritization matrix (effort, risk, payback)
Build-vs-buy recommendation per use case
12-month roadmap with budget bands
Governance & risk baseline
5 weeks
average time from kickoff to a signed roadmap
3.2
average use cases prioritized for year-one build
92%
of roadmaps result in a funded pilot within 90 days
Agents
02
Agents
8–14 weeks
Agent & Workflow Automation
Production-grade AI agents that execute multi-step operational workflows, with human review where it matters.
Workflow mapping & agent scope definition
Agent architecture & tool integration
Guardrails & human-in-the-loop review points
Evaluation harness (pre- and post-launch)
Production deployment & monitoring
63%
average reduction in manual processing time
9 weeks
average time to first production agent
24/7
monitored once the agent is in production
Knowledge
03
Knowledge
6–12 weeks
Knowledge & RAG Systems
Retrieval-augmented assistants that answer from your own documents, policies and systems of record, with citations.
Knowledge source mapping & access design
Retrieval pipeline & chunking strategy
Grounding & citation layer
Evaluation against a gold question set
Rollout & usage analytics
38%
faster average resolution time on grounded queries
94%
answer accuracy against a held-out evaluation set
10 weeks
average time to production
Documents
04
Documents
10–16 weeks
Document AI
Extraction, classification and routing pipelines that turn unstructured documents into structured, trustworthy data.
Document taxonomy & sample audit
Extraction & classification pipeline
Confidence thresholds & exception routing
Human review queue
Integration into downstream systems
91%
average straight-through processing rate
4,200 hrs
typical monthly staff hours reclaimed at enterprise scale
14 weeks
average time to production
Data & MLOps
05
Data & MLOps
12–20 weeks
Data & MLOps Foundations
The pipelines, feature stores and monitoring that let models stay accurate after launch, not just at demo time.
Data pipeline & feature store design
Model training & evaluation infrastructure
Deployment & rollback tooling
Drift & performance monitoring
Cost and latency budgets
6.4 pts
average improvement in forecast accuracy (MAPE)
99.95%
average uptime across systems we operate
16 weeks
average time to production
Enablement
06
Enablement
8–12 weeks
AI Enablement & Training
Hands-on training, governance playbooks and adoption support so your team owns what we build.
Role-based training curriculum
Governance & responsible-AI playbook
Internal champions program
Usage dashboards & adoption targets
Handoff & ownership transfer
71%
average weekly active use within 90 days of rollout
5.5 hrs
average hours saved per person per week
12 weeks
average time to firmwide rollout
04 · Questions
Stack questions,
answered before proof.
Everything we build lives in your cloud account and your repositories, on the models that fit the job.
01
How do services combine across one engagement?
Most engagements start with one service, often an Assessment or a single Pilot, and add others as the roadmap calls for them. A RAG build often pulls in Data & MLOps Foundations once it is in production.
02
Can we start with a Pilot instead of an Assessment?
03
Do you ever recommend against building something?
04
What if our use case doesn’t fit neatly into one service?
05
How is pricing structured?
05 · The stack, in production
Each capability,
doing real work somewhere.
06 · Start
Fifteen minutes
tells us which pieces.
Describe the workflow. We’ll tell you which parts of the stack actually apply, and which don’t.