whitepaper/Context Engineering
Context Engineering
Build the context layer AI needs to do real work.
We connect your records, rules, and permissions into governed context that AI can retrieve and cite. It is the core technical layer of every Custom AI Development build, and available as a specialist engagement.
45 minutes with our AI experts to find your highest-value opportunity and a practical next step. No preparation required.
1Business sources
- CRMAccount, contacts, deal stage
- EnrichmentFirmographics, sites, headcount
- Sequence historyPast touches and replies
- Campaign dataResults by segment
- Messaging guideApproved claims and tone
2Retrieval and rules
- MatchContact to account by domain
- QualifyFit score of 70 or higher
- SuppressAny touch in the last 21 days
- FreshnessEnrichment under 30 days old
- PermissionsOnly fields this role may read
3Governed context
Account #2210 · Northline Supply
- Segment
- Mid-market manufacturingCRM
- Fit score
- 82 · qualifiesRule
- Last touch
- 46 days agoSequences
- Trigger
- Second plant openingEnrichment
- Claims
- 2 of 5 applyGuide
- Withheld
- Personal phonePolicy
4AI output
Research brief and first-touch draft
"Saw the second plant is opening this spring. Teams adding a site usually hit scheduling gaps first..."1,4
- 1Segment · CRM
- 4Trigger · Enrichment
- 7 of 7 checks pass
- Every claim cites a field
- Waits for rep approval
Evaluation: 24 test cases run on every rule or source changeFeedback: rep edits flag rules to review
Case study · Digital marketing agency
Personalized outreach, built on connected revenue context.
A marketing agency's best outbound depended on people gathering research across separate revenue systems. We connected that context into a governed prototype for qualification and personalized research. Client name withheld; references on request.
Before
- Research spread across revenue systems
- Account briefs assembled by hand
- Campaign preparation limited capacity
After
- Connected account and contact context
- Research and outreach prepared for review
- Human approval ahead of every send
SystemsHubSpot, Apollo, Clay, sequence history, and campaign data
What we builtConnected business context, qualification and suppression rules, account research, and personalized outreach with human approval gates.
DeliveryA working prototype on the client's live revenue stack.
92%Evaluation result across 24 test cases
2 weeksAudit to working prototype in this engagement
≈$2.1MModeled 12-month revenue opportunity, pending calibration
What production quality means here
Reliable AI output is a property of the context, not the prompt.
A prototype can work on a good day with a long prompt. Production needs context that is correct, permitted, current, and tested every time it changes.
- 01
Every field has a source
Context records carry the system or rule each value came from, so outputs can cite them and reviewers can check them.
- 02
Rules run before the model
Matching, qualification, freshness, and permissions are enforced at retrieval. The model never sees what the role may not use.
- 03
Tested on every change
Evaluation cases run whenever a source, rule, or model changes, so a schema change upstream is caught before it reaches users.
Scope
A context layer your AI systems can build on.
Inside a Custom AI Development build, this work is part of the scope. As a specialist engagement, it prepares the context for the systems you or we build next.
Connect
- Source systems mapped and connected
- Entity matching across records
- Access scoped to each role
Structure and govern
- Business objects and relationships
- Qualification, suppression, and freshness rules
- Source citations on every field
Evaluate
- Evaluation cases from real records
- Regression checks on every change
- Documentation for your team
Context engineering does not include building the agent or application itself; that is Custom AI Development. Scoped per engagement around one workflow first, at a fixed or milestone-based price.
One workflow first, then context the next system can reuse.
If the starting point is unclear, AI Strategy and Opportunity Assessment helps choose the workflow.
01Define
Define the workflow
We confirm the output, the business owner, the source systems, and what good looks like.
A systems map and evaluation criteria.
02Connect
Connect and govern
We connect sources, structure the context, and encode the rules and permissions the work depends on.
A governed context layer with citations.
03Prepare
Test and hand over
We run evaluation cases, fix what fails, and prepare the layer for agents, review steps, and write-backs.
A tested foundation for production AI.
Common questions
How does this relate to Custom AI Development?
Context engineering is the layer every custom AI system depends on. In a Custom AI Development engagement it is part of the build. On its own, it prepares the context so the next agent or application starts from a tested foundation.
Is this the same as retrieval-augmented generation?
Retrieval is one part. Context engineering also covers which records match, which rules apply, what each role may read, how fresh the data must be, and how all of it is tested.
Where does our data live?
In your systems and your cloud accounts. We connect to sources with scoped access agreed with IT, inside your security boundary.
Do we need clean data first?
No. We start with the records one workflow needs and fix the gaps that matter for that output. Broader cleanup is scoped only when the evidence supports it.
What do we own at the end?
You retain the deployed system and documentation and can keep operating it through your own accounts after a documented handoff.
Context Engineering
Find the first workflow worth giving better context.
45 minutes with our team. We look at the output you need, the systems behind it, and what context the AI would need to produce it reliably.
45 minutes
No prep required
A practical next step
Your request is received.
Choose a time for your 45-minute session now, or our team will follow up to arrange it.
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