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Solutions · Custom GPTs & Assistants

Custom GPTs · trained on your data

Custom GPTs and AI assistants trained on your business — internal tools, customer-facing copilots, eval-tracked

Most "custom GPTs" are a system prompt and a vector store stitched together in 30 minutes. That is not a product. We build custom GPTs the way they should be built: scoped task definition, eval suite, knowledge ingestion pipeline with proper chunking, tool integrations to your stack, observability, and ongoing tuning. Internal-team copilots, customer-facing assistants, and specialized GPTs for sales coaching, content review, and ops decisions.

Book the free audit→See pricing

90%+

accuracy after tuning

4–6 wk

production-ready

30–50%

cost reduction (multi-model)

gpt.dgcore — Custom GPT pipeline

LIVE

▸ Production GPT · RAG with reranking · cited

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Ingest

12,400 docs

RUNNING

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Embed

Vector store · pgvector

QUEUED

Query

"How did we handle Q3 refunds?"

QUEUED

⌬

Retrieve

6 cited sources

QUEUED

✓

Answer

92% accuracy · cited

QUEUED

Eval suite accuracy

92%

Avg cost / query

$0.018

Certified on the platforms you already use80+ builds shipped

GoHighLevel

HubSpot

n8n

Make.com

Zapier

Klaviyo

Airtable

▸ Our verdict on Custom GPTs & Assistants

Custom GPTs are useful when (1) the task is well-scoped and repeatable, (2) the underlying data is structured enough to retrieve from accurately, and (3) you have an eval criterion that lets you measure if the agent is doing better than baseline. Done well, they save real time and lift output quality. Done badly, they hallucinate and you stop trusting them. Eval suite from day 1 is the moat.

What we deliver

What our custom GPT engagements cover

Standard scope. Custom scope available on the audit.

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Knowledge ingestion + RAG

Proper chunking, embedding, vector store (Pinecone, Weaviate, pgvector), reranking layer, freshness handling. Not "drop docs in a folder".

⌬

Tool integration

CRM lookup, calendar booking, email drafting, Slack messaging, custom internal APIs. The agent does work, not just answers questions.

✦

Eval suite + observability

Eval suite built from real prompts. Per-query logging, accuracy tracking, cost-per-query tracking, weekly tuning cycle. The boring infrastructure that makes AI trustworthy.

🌐

Multi-model routing

GPT-4o for general reasoning, Claude for long-context analysis, Gemini for cheap classification. Task routed to the right model automatically. 30–50% cost reduction vs single-model.

Engagement model

From audit to live Custom GPT build in 4 steps

Same engagement shape as every digicore101 build. Predictable timeline, predictable cost, no scope creep.

017 days

AI Audit

60-min strategy session, stack map, leak analysis, costed roadmap. Vendor-neutral — yours to keep.

·Architecture diagram
·Build sequence
·Cost + timeline lock

025–10 days

Architecture

Custom GPT schema, automations on paper, integration map, AI agent personas.

·Approved schema
·Sign-off on flows
·Migration plan if applicable

032–6 weeks

Build & Deploy

Weekly demos, staged rollout, full handoff documentation. You own everything.

·Live system
·Loom walkthroughs
·Team training session

04Ongoing

Train & Support

Retainer keeps the Custom GPT stack tuned, monitored, and improving — not just running.

·Slack channel
·Weekly tune cycle
·Monthly reporting

The math

Cheap stitched GPT vs production-grade custom GPT

A "custom GPT" stitched together in 30 minutes is not a product — it is a demo. Production-grade custom GPTs need eval suites, observability, and proper RAG to be trustworthy at scale.

✓Stitched GPT: $0 setup · ~50–60% accuracy · no eval, no logs

✓Production GPT: $4–8k setup · 90%+ accuracy · eval-tracked, observable

✓Cost per query: $0.001–0.05 depending on model + length

✓Multi-model routing (cheap classifier + expensive reasoner) reduces cost 30–50%

Production accuracy by build approach

Stitched in 30 min~55%

No eval · no observability · breaks at edges

Production GPT · day 1~80%

Eval suite running · RAG with reranking

After 60 days tuning~92%

Tuned against eval failures · multi-model routing

The math

+37pts accuracy

eval suite is the moat

Custom GPTs & Assistants vs Digicore AI

How Custom GPTs & Assistants compares to Digicore AI

A side-by-side on what each platform actually does. Vendor-neutral — we work in both.

CapabilityCustom GPTs & AssistantsDigicore AI

Knowledge ingestionDrop docs in a folderChunked + embedded + reranked

Tool useNone or basicCRM + calendar + email + custom APIs

Eval suiteNoneBuilt from real prompts · tracked weekly

ObservabilityNo logsPer-query logging · accuracy tracking · cost tracking

Model choiceGPT-4 onlyGPT-4o + Claude + Gemini · routed by task

Best forQuick demosProduction team copilots

Recent custom GPT work

How real teams used this

Names anonymized where requested.

Sales coach

Agency · sales-call review GPT · −22 hr/wk founder time

GPT trained on 200+ winning + losing call recordings. Reviews every call, flags coaching moments, drafts follow-up tweaks. Founder review time dropped from 30 min/call to 4 min.

200+ calls−22 hr/wkSales coach

Brand voice

Course creator · brand-voice content GPT

Trained on founder's 4 years of writing. Drafts emails, captions, and long-form in voice. Founder approves; AI ships. Saved 14 hr/wk.

Brand voice−14 hr/wk4-yr corpus

Support

SaaS · support triage GPT · 73% deflection

GPT trained on 12 months of resolved tickets. Reads inbound, classifies, drafts responses, escalates the rest. 73% tier-1 deflection · CSAT 4.6.

73% deflection12 mo ticketsCSAT 4.6

Analyst

B2B · revenue analyst GPT · weekly insights

GPT with read-only access to CRM + Stripe + GA4. Generates weekly revenue commentary with anomaly detection. Founder gets the insights without the 6-hour analysis.

Revenue analystWeekly autoAnomaly detection

When this fits

Honest scope — and who shouldn't engage

Custom GPTs work when scope is tight, eval is real, and the data exists.

✓ Engage when

Task is well-scoped and repeatable
"Review sales calls", "draft brand-voice emails", "triage tickets" — clear win.
You have 6+ months of training data
Real examples are the moat. New domains are harder.
You can define an eval criterion
"Did the GPT do better than baseline on these 50 examples?" — if you can answer, you can measure.

✗ Don't engage when

"Do anything" assistant scope
Wide scope = poor accuracy = lost trust. Tight scope wins every time.
No training data + no eval criteria
Without those, you are flying blind. We will not build it.
Tasks that need consistent perfect accuracy
Legal contracts, medical advice, financial calculations — domain-specific tools beat general GPTs.

Pricing depends on scope

Every Custom GPTs & Assistants build is a different shape.

We don't quote off a feature checklist — we quote off your stack, your bottleneck, and the build phases that actually move revenue. The audit is the front door: free, 7-day costed roadmap, vendor-neutral.

Book the free audit→See full pricing

FAQ

Questions before we start

How is this different from a ChatGPT custom GPT?+

ChatGPT custom GPTs are fine for personal use. They lack the eval suite, the tool integrations, the observability, and the ability to run inside your team's actual workflows. We build the production-grade equivalent that lives in Slack, your CRM, your help desk — wherever the team actually works.

Do you build the model yourselves?+

No — we use frontier models (GPT-4o, Claude Sonnet 4.6, Gemini). Training your own model is almost never the right call below $10M ARR. We build the wrapper that makes the frontier model useful for your specific business.

What about hallucinations?+

Three layers of mitigation. (1) RAG with reranking + citation requirement — the agent has to cite source documents for factual claims. (2) Eval suite catches regressions before deploy. (3) Confidence threshold — below it, the agent says "I do not know" rather than guess. Hallucination is not zero, but it is manageable.

How long until it is useful?+

Typical 4–6 weeks. Week 1: scope + eval criteria. Week 2–3: knowledge ingestion + agent build. Week 4: shadow testing against eval suite. Week 5–6: live deployment with daily review. Day-1 accuracy is typically 75–82%; tunes to 90%+ over the first 60 days.

What does it cost?+

Full Build $1,997+ + $197–597/mo (multi-tool, eval suite, observability). Per-query LLM costs typically $0.001–0.05 depending on model + length.

Keep exploring

Where Custom GPTs & Assistants fits in the bigger picture

Most engagements layer 2–3 platforms with a service shape. These pages map the surrounding territory.

Ready when you are

Ready to scope your Custom GPTs & Assistants build?

Book the free AI System Audit. We map your stack, find the leaks, and deliver a build roadmap in 7 days. Vendor-neutral.

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