AI-powered systems, teams and tools — wired into the work you already do
SakhiSoft is an IT services company that builds AI into production software. Not demos. Systems with evaluation sets, human review where it matters, guardrails and monitoring — the parts that decide whether an AI feature is still working six months after launch.
Each one starts from a problem someone in your business is currently solving by hand. If we cannot name that person and that problem, we will tell you it is not ready to build.
AI Copilots & Chat Assistants
Your answers already exist — in Confluence, a policy PDF, six years of tickets, a database nobody queries. Staff still ask a colleague, and the colleague is in a meeting.
What we build
Retrieval-augmented assistants grounded in your own corpus, with citations back to the source paragraph
Role-aware retrieval so a user only ever retrieves documents their account is already permitted to read
Tool-calling copilots that can read from and write to your CRM, ticketing or internal APIs
Escalation paths to a human when confidence or retrieval coverage is low
Embedded chat surfaces: in-app widget, Slack/Teams bot, or an internal agent console
Support and ops teams stop paging subject-matter experts for questions the documentation already answers, and every answer carries a link to the clause it came from.
Healthcare & EMSTelecom & ISPStartups & SaaS
Document Intelligence & OCR
Invoices, claims, facesheets and PODs arrive as scans, faxes and phone photos. Someone re-types them into a system, at a few minutes each, with a typo rate nobody measures.
What we build
Extraction pipelines that turn PDFs, scans and photos into typed, schema-validated records
Layout-aware parsing for tables, multi-page documents and rotated or low-quality scans
Per-field confidence scores, so only genuinely uncertain fields go to a person
Human-in-the-loop review UI showing the source page with the extracted value highlighted in place
Corrections captured as labelled data and fed back into the evaluation set
High-confidence fields post straight through to the system of record; reviewers spend their time on the genuine exceptions rather than on every document.
Most AI projects do not fail at the model. They fail because nobody defined correct, nobody measured drift, and nobody could explain an output three months later. This is the checklist we work to.
01
An evaluation set before a single prompt
We start by collecting real inputs and agreeing the correct outputs with your subject-matter experts — typically 50-200 cases including the awkward ones. That set is the definition of done. Without it, 'the AI got better' is an opinion.
02
Offline evaluation gates every change
Prompt edits, model swaps and chunking changes run against the evaluation set in CI and report accuracy, retrieval recall, latency and cost per case. A change that regresses a metric does not merge — the same bar we apply to a failing unit test.
03
Human-in-the-loop where the cost of being wrong is real
Confidence thresholds decide the routing: high-confidence outputs post through, everything else lands in a review queue with the source document and the model's reasoning attached. Reviewer corrections are captured as labelled data and flow back into the evaluation set.
04
Guardrails on both sides of the model
Inbound: PII redaction, injection filtering, and retrieval scoped to the caller's existing permissions. Outbound: schema validation, citation checks against the retrieved context, and refusal when retrieval coverage is too thin to answer honestly.
05
Monitoring that watches quality, not just uptime
A 200 OK from a model API tells you nothing about the answer. We track refusal rates, retrieval-miss rates, output-validation failures, latency percentiles and cost per request, and alert on drift in the distribution of inputs.
06
Auditability by default
Every inference is logged with its prompt version, model version, retrieved chunks, output, and any human decision applied to it. When someone asks in nine months why the system did what it did, the answer is a query, not an investigation.
07
Deterministic code wherever a model is not required
Models are used for judgement, ambiguity and language. Arithmetic, eligibility rules, routing tables and anything an auditor will read stays in ordinary, testable code. This is the single biggest reliability decision on most AI projects.
08
A rollback path you can use at 2am
Model and prompt versions are pinned and deployable independently of application code, behind flags. Reverting a bad prompt should not require a release train, and disabling an AI feature entirely should be a switch anyone on-call can flip.
AI tech stack
The tools we reach for — and why
We are not loyal to a vendor. Model and infrastructure choices are made against your evaluation set, your latency budget and your data-residency rules, then revisited when the numbers change.
Models & providers
Hosted frontier models where quality leads, open-weight models where cost, latency or data residency leads.
The engineering is transferable; the domain rules are not. These are the sectors where we already know the workflows, the integrations and the compliance constraints.
Healthcare & EMS
Dispatch systems, patient portals, OCR for facesheets, and HIPAA-aware workflows.
Customer portals, recurring billing, UISP/UNMS integrations, and usage dashboards.
AI solutions that apply
AI Copilots & Chat Assistants
Workflow & Process Automation
LLM Integration & Fine-tuning
Data Engineering & RAG Platforms
MLOps & AI Governance
Startups & SaaS
From MVP to scale-up: rapid prototypes, investor-ready demos, and production hardening.
AI solutions that apply
AI Copilots & Chat Assistants
Predictive Analytics & Forecasting
Recommendation & Personalization
LLM Integration & Fine-tuning
Data Engineering & RAG Platforms
FinTech & Subscription Products
PCI-conscious payment flows, invoicing, and recurring revenue analytics.
AI solutions that apply
Document Intelligence & OCR
Predictive Analytics & Forecasting
Recommendation & Personalization
MLOps & AI Governance
Qualifier
Is your use case actually a fit?
We would rather lose the project than build something that quietly stops working. Read both columns honestly — the right-hand one is the one that saves you money.
Worth building
The task is repetitive, language- or document-heavy, and a competent person could do it with the information already available to the system.
You can produce examples of the work being done correctly — past tickets, reviewed documents, historical decisions. That is your evaluation set and your training data.
A wrong answer is recoverable: it gets caught by review, a rule, or a human before it reaches a customer or a ledger.
There is an owner on your side who can arbitrate what 'correct' means when two reviewers disagree.
Success has a number attached — handling time, straight-through rate, deflection rate, forecast error — that someone already tracks.
We will tell you not to
The rules are fixed, written down and unambiguous. That is a deterministic system with a test suite, and it will be cheaper, faster and auditable. We will tell you so.
A single wrong output is unrecoverable — money moves, a dose is administered, a contract executes — with no review step you are willing to fund.
The underlying data does not exist, is not accessible, or is so inconsistent that your own experts cannot agree on the right answer.
The goal is to be able to say the product has AI in it. That is a marketing brief, and it will not survive contact with production.
Accuracy needs to be effectively perfect on unbounded natural-language input. Nothing available today clears that bar honestly.
Still unsure? That is the normal state. A short discovery call is usually enough to tell whether your problem needs a model, a rules engine, or just a better form.
Bring us the workflow, not the buzzword
Tell us which task is eating your team's week, what “done correctly” looks like, and what data you already have. We will come back with a scoped first phase, an evaluation plan, and an honest read on whether AI is the right tool for it at all.