Implementation
Add context integrity without rebuilding your AI stack.
FreshContext sits between retrieval and action. We integrate the existing judgment layer into a bounded workflow, test it against agreed acceptance criteria, and hand over a documented implementation.
- input
- retrieval / tools / memory
- judgment
- FreshContext Core
- output
- decision-ready context
- consumer
- model / agent / app
What we integrate
One judgment layer, fitted to your workflow.
FreshContext does not replace your retriever or model. It evaluates candidate context on the way in and can leave independently checkable evidence behind.
Candidate context
Evaluate context your existing retriever, agent, database or toolchain already produces.
Freshness and confidence
Apply source-aware temporal decay, date confidence and failure honesty instead of treating every result as equally current.
Decision output
Return structured decisions such as use, cite, refresh, verify, background, watch or exclude, with reasons attached.
Verification path
Where the deployment uses ledger-backed verdicts, connect the signed Ed25519 attestation and offline-verification path.
Standard pilot
Fixed scope. Objective acceptance.
The first engagement is deliberately bounded so both sides know what success means before work starts.
Included
One bounded workflow, architecture-fit review, integration/configuration, deterministic acceptance tests, deployment or handover documentation, and a short defect-support window.
Customer dependencies
A technical contact, test or staging access where required, representative payloads, and prompt answers to implementation blockers. Customer-caused delay moves the timetable.
Typical engagement range
Buy the level of implementation you actually need.
These are working commercial ranges, not commodity package prices. Scope, dependencies and acceptance criteria determine the final proposal.
| Engagement | Typical range | Purpose |
|---|---|---|
| Context Integrity Assessment | US$750–1,500 | Architecture review, sample evaluation, fit map, risk/gap memo and implementation estimate. |
| Single-workflow integration | US$2,500–5,000 | One bounded RAG or agent workflow with acceptance tests and handover. |
| Private / multi-workflow implementation | US$7,500–15,000+ | Multiple workflows, private deployment, verification, operating evidence and training/handover. |
| Build-to-spec / strategic implementation | US$20,000+ | Buyer-specific gaps built under milestone and acceptance structure. |
| Strategic licence / acquisition | Case-specific | Defined rights, assets, infrastructure, transition and future-development capability. |
Who this is for
Teams already carrying context risk.
The buyer is usually the team that owns reliability, retrieval quality, agent execution or governance — not a generic software shopper.
AI / platform leads
Add a judgment layer without rebuilding the retrieval or model stack.
RAG / search teams
Apply temporal pressure and source confidence where semantic ranking alone is insufficient.
Governance / risk teams
Preserve inspectable evidence of why context was trusted, questioned, refreshed or excluded.
AI integrators
Embed a reusable context-integrity primitive inside client systems instead of re-inventing it project by project.
Proof
Inspect it before speaking to us.
A commercial conversation should start after the technology has already survived basic scrutiny.
Founder-led implementation
Built with an operations mindset.
FreshContext is built by Immanuel Gabriel, an applied physicist with industrial laboratory and mining-operations experience, including shift supervision. The operating instinct is simple: evidence, failure states and handovers should be explicit rather than assumed.