SECTOR / 01
E-commerce.
Product recommendations, support deflection, return processing, review analysis. Revenue recovered from every touchpoint.
[04] Tech Department
AI
AI
Integration.
We embed AI directly inside your operational stack — no demoware, no widgets. Agents that close tickets, qualify leads, draft contracts, and run processes your team never has to touch.
Module / 04
[01] How it works
Built in four moves.
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01
Identify
Find the workflows where AI replaces humans, not assists them. We map the unit economics first — cost per task, error rate, time lost.
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02
Architect
Choose the right model class, retrieval strategy, and eval suite. Vendor-neutral. ROI-driven. We design before we build.
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03
Train
Fine-tune or RAG over your data. Build the guardrails. Set accuracy thresholds and edge-case benchmarks before anything goes live.
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04
Deploy
Ship into production with full observability. Accuracy, latency, cost — tracked live. Human-in-the-loop fallbacks built in from day one.
[02] What we integrate
Four systems. One signal.
Your business generates intelligence all day.
Most of it never gets processed. We close that gap.
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01
Support & service agents
Tickets classified, drafted, and resolved. Sentiment flagged. Escalations routed. Your team handles exceptions only.
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02
Sales intelligence
Calls transcribed and summarised. Leads scored. Objections surfaced. Deal summaries sync to CRM before the rep logs off.
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03
Document processing
Contracts reviewed, clauses flagged, data extracted. Compliance checked at ingestion. Legal reviews exceptions — not the stack.
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04
Internal knowledge agents
Staff query company data in natural language — policies, docs, Notion, Confluence. Instant. Accurate. No ticket needed.
[03] Stack we run
We pick the right model. Not the popular one.
Vendor-neutral. Eval-driven.
Zero lock-in.
Right model for the job. Not the one we have a deal with.
[04] Human → Machine
Before. After.
Before
Support team reads every ticket, categorises by hand, routes to the right queue. 4+ hours per agent per day. Hot issues wait in line.
After
AI classifies, drafts the reply, routes, flags sentiment spikes. Team reviews exceptions only. 80% resolved without human touch.
Before
Sales rep listens to every call, manually logs notes, sets next steps in CRM. 3+ hours per rep per week. Half the calls never get logged.
After
AI transcribes, summarises, extracts actions, syncs to CRM on call end. Rep leaves the call and the record is already written.
Before
Legal reviews every contract for clause deviations. Two days per deal. Bottleneck on every close. Non-standard terms slip through anyway.
After
AI flags non-standard clauses, risk-scores each section, generates redline. Legal reviews exceptions — not the whole document.
Before
Staff email HR or dig through intranet for answers. 30 minutes per query on average. Knowledge spread across five tools no one can search.
After
Internal AI agent answers from live docs, policies, Notion, Confluence — in seconds, with citations. Zero wait. Zero tickets.
Before
AI errors surface from customer complaints. No logging, no thresholds, no fallback. You find out what broke after damage is done.
After
Every agent has confidence thresholds, accuracy monitoring, human-in-loop fallback. Errors auto-route. Eval loop catches regressions before users do.
[05] Who it fits
Built for teams with data to spend.
SECTOR / 02
B2B SaaS.
Churn signals, onboarding agents, trial-to-paid intelligence, deal summaries. AI embedded in the revenue motion.
SECTOR / 03
Legal & Finance.
Contract review, compliance checks, document extraction, audit prep. Accuracy requirements met before launch.
SECTOR / 04
Healthcare.
Documentation automation, triage assistance, scheduling, clinical summaries. HIPAA-compliant by architecture, not afterthought.
[06] Why INHOUSE
Not another demo.
Vendors sell you demos.
We hand you a system.
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01
Embedded, not bolted on.
AI lives inside your workflows — not beside them as a widget. Same data, same APIs, same stack you already run.
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02
Your data stays yours.
No training on your production data without consent. Fine-tuned models are served from your infra. We document the data lineage.
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03
Measurable from day one.
Eval suites before go-live. Every deployment has accuracy targets, latency SLAs, and regression tests. You see the numbers.
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04
Vendor-neutral by design.
We pick the right model for the job — not the one we have a deal with. Switching models later touches one layer, not everything.
[07] Questions we get asked
Before you email.
Is our data used to train the AI?
No. We separate your data from model training entirely. If we fine-tune, it uses isolated datasets — never mixed with third-party data. Models are served from your own infrastructure. Data lineage is documented and auditable. You own everything.
Which AI models do you work with?
Claude, GPT-4o, Gemini, Llama 3, Mistral — and their open-source variants. We choose per use case based on accuracy benchmarks, latency requirements, and cost profile. Not per vendor preference. You see the eval results before we commit to a model.
How do you measure if the AI is actually working?
Eval suites built before launch, not after. Every model has accuracy thresholds, edge-case benchmarks, and regression tests that run on every deployment. We track accuracy, latency, cost, and confidence score in production. If anything drifts, you know before users do.
What happens when the AI gets something wrong?
Every agent has a confidence threshold and a human-in-the-loop fallback. Low-confidence outputs don’t go to users — they route to a human queue automatically. Errors feed the eval loop and inform the next fine-tune cycle. Mistakes get rarer over time.
How fast can you ship the first agent?
First agent in production: typically 3–5 weeks from audit to deploy. That includes identify, architect, train, and a monitored launch. Complex multi-agent systems or regulated environments run longer — always scoped and priced before sign-off.
Do we own the integration?
You own the integration layer, prompts, fine-tuning datasets, eval suites, and serving infrastructure. Model weights from providers stay with providers — we’re explicit about that distinction in the contract. If we exit tomorrow, your agents keep running.
"AI embedded in your stack isn’t a feature. It’s infrastructure."
— INHOUSE AI