Service
AI Integration and Enablement
We add language model features to the systems you already run: retrieval over your documents, drafting inside your tools, and private or on device model options where data cannot leave.
The problem
What usually goes wrong
The systems that hold your work are rarely the systems where AI is easy to add. Your quotes live in one place, your documentation in another, your history in a database nobody queries directly.
So staff copy text into a chat tool, paste the answer back, and the useful part never becomes part of the process. Nothing is logged, nothing improves, and sensitive material travels through a consumer account.
Enablement work puts the model inside the tool where the job happens, reading your material, under your access rules.
Scope
What we deliver
Retrieval over company documents
Contracts, manuals, policies, past proposals and ticket history are indexed with metadata so answers are grounded in your material and can cite the source document.
In product drafting and summarisation
Reply drafts, meeting summaries, quote descriptions and record summaries generated in place, with the person editing rather than starting from blank.
Classification and routing
Inbound messages, tickets and form submissions tagged, prioritised and routed by a model where rules cannot express the distinction.
Extraction from documents
Structured fields pulled out of PDFs, invoices, purchase orders and email, validated against a schema before anything is written to a record.
Private and on device options
Where material cannot leave your environment, we design against a model you host or run locally and keep the index inside the network.
Cost and usage controls
Per feature budgets, caching of repeated work, and truncation policies so usage is predictable rather than discovered on an invoice.
Method
Technical approach
Indexing starts with the document inventory. We record where each source lives, how it changes, who may see it, and whether it carries metadata worth filtering on. Access control is applied at retrieval, so a user never receives a passage from a document they could not open directly.
Chunking follows structure. Headings, clauses and record boundaries make better chunks than fixed windows, and each chunk carries its source, section and effective date so the answer can cite them.
Extraction is validated. The model proposes structured output, a schema check rejects anything malformed, and a confidence threshold decides whether the record is written directly or queued for review.
Caching matters more than model choice for cost. Repeated questions, unchanged documents and identical summaries are served from cache with an invalidation rule tied to the source record.
Private deployment is a design decision made early, because it changes the index, the latency budget and the hardware. We specify it during planning rather than retrofitting it.
Handover
Deliverables
Everything below is yours at delivery, including the intellectual property. Sawhorse keeps nothing.
- 01Document index with access filtered retrieval
- 02Feature level integration inside your existing application or tool
- 03Extraction pipelines with schema validation and review queues
- 04Classification and routing rules with a measured accuracy baseline
- 05Caching, budget and rate limiting layer
- 06Private or on device model configuration where required
- 07Evaluation set and comparison script
- 08Source code, deployment guide and operating documentation
Related
Systems this service builds
- Document ManagementSystems that store, version, index and retrieve your documents, with permissions that hold and retrieval that finds the right clause rather than the right file name.
- AI Agents and AssistantsAssistants embedded in your product or operation that answer from your data, take scoped actions through typed tools, and escalate to a person when they should.
- Workflow Automation PlatformsAn orchestration layer that runs your cross system processes with retries, visibility and controls, instead of scripts on a laptop and rules in one person email client.
FAQ
Questions people actually ask
Can you add this to software we did not build with you?
Usually yes, if it has an API or a database we can read. Where the system is closed, we build the feature alongside it and integrate through whatever export, webhook or file drop it supports.
Will staff see documents they are not allowed to see?
No. Retrieval filters on the same permissions as the source system, applied before the model sees anything. A passage from a restricted document is never placed in the context.
How do you keep usage costs predictable?
Caching, context truncation policies and per feature budgets. Usage is logged by feature so you can see where spend goes and cap it.
What does an on device option actually mean here?
The model runs on hardware you control, either a server inside your network or the end user machine, and the document index sits alongside it. Nothing about the content of a request leaves your environment.
How current are the answers?
As current as the index. We set an update path for each source, either event driven when a record changes or on a schedule, and the answer can show the effective date of the document it cites.
What if the extracted data is wrong?
Schema validation rejects malformed output, and a confidence threshold routes uncertain extractions to a review queue rather than writing them into a record.
Let’s talk about the system you need.
Tell us where the current process breaks down. We can map the workflow, examine the integration requirements and define a practical scope for the build.
- A mutual NDA is signed before project details are shared.
- Scope is fixed in writing before any build starts.
- You own the source code, the IP and the documentation at delivery.
- Support plans are available and tailored on a discovery or technical call.